Secure Cloud Data Storage Using Hybrid Encryption Algorithms — Complete Documentation & Simulation

Overview & System Context

Secure Cloud Data Storage Using Hybrid Encryption Algorithms: Comprehensive Web-Sourced Technical Investigation Exhaustive Research Dossier · Pure Web-Scraped Content: ~8519 Words · Real Software Screenshots: 3 1. Executive Overview & Technical Background Cloud storage is a model of computer data storage in which data, said to be on "the cloud", is stored remotely in logical pools and is accessible to users over a network, typically the Internet. The physical storage spans multiple servers (sometimes in multiple locations), and the physical environment is typically owned and managed by a cloud computing provider. These cloud storage providers are responsible for keeping the data available and accessible, and the physical environment secured, protected, and running. People and organizations buy or lease storage capacity from the providers to store user, organization, or application data. Cloud storage services may be accessed through a colocated cloud computing service, a web service application programming interface (API) or by applications that use the API, such as cloud desktop storage, a cloud storage gateway or Web-based content management systems.

Cloud storage is based on highly virtualized infrastructure and is like broader cloud computing in terms of interfaces, near-instant elasticity and scalability, multi-tenancy, and metered resources. Cloud storage services can be used from an off-premises service (Amazon S3) or deployed on-premises (ViON Capacity Services). There are three types of cloud storage: a hosted object storage service, file storage, and block storage. Each of these cloud storage types offer their own unique advantages.

Examples of object storage services that can be hosted and deployed with cloud storage characteristics include Amazon S3, Oracle Cloud Storage and Microsoft Azure Storage, object storage software like Openstack Swift, object storage systems like EMC Atmos, EMC ECS and Hitachi Content Platform, and distributed storage research projects like OceanStore and VISION Cloud. Alibaba Cloud is another example of object storage services or OSS that is fully managed, scalable, secure, and cost-effective cloud storage that is made for storing and accessing large amounts of unstructured data, similar to Amazon S3 and Microsoft Azure Storage. OSS supports bucket-based storage, global access and high durability, making it a standard cloud object storage platform. Examples of file storage services include Amazon Elastic File System (EFS) and Qumulo Core, used for applications that need access to shared files and require a file system. This storage is often supported with a Network Attached Storage (NAS) server, used for large content repositories, development environments, media stores, or user home directories. A block storage service like Amazon Elastic Block Store (EBS) is used for other enterprise applications like databases and often requires dedicated, low latency storage for each host. This is comparable in certain respects to direct attached storage (DAS) or a storage area network (SAN). Cloud storage is: Made up of many distributed resources, but still acts as one, either in a federated or a cooperative storage cloud architecture Highly fault tolerant through redundancy and distribution of data Highly durable through the creation of versioned copies Typically eventually consistent with regard to data replicas Figure 1: Architecture Of Cloud Storage Encryption Management Encryption For Data ...

Security of stored data and data in transit may be a concern when storing sensitive data at a cloud storage provider. Outsourcing data storage increases the attack surface area. Cloud storage is a rich resource for both hackers and national security agencies. Because the cloud holds data from many different users and organizations, hackers see it as a very valuable target.

When data has been distributed it is stored at more locations increasing the risk of unauthorized physical access to the data. For example, in cloud based architecture, data is replicated and moved frequently so the risk of unauthorized data recovery increases dramatically. Such as in the case of disposal of old equipment, reuse of drives, and reallocation of storage space. The manner that data is replicated depends on the service level a customer chooses and on the service provided. When encryption is in place it can ensure confidentiality. Crypto-shredding can be used when disposing of data (on a disk). The number of people with access to the data who could be compromised (e.g., bribed, or coerced) increases dramatically. A single company might have a small team of administrators, network engineers, and technicians, but a cloud storage company will have many customers and thousands of servers, therefore a much larger team of technical staff with physical and electronic access to almost all of the data at the entire facility or perhaps the entire company. Decryption keys that are kept by the service user, as opposed to the service provider, limit access to data by service provider employees. As for sharing multiple data in the cloud with multiple users, a large number of keys has to be distributed to users via secure channels for decryption, also it has to be securely stored and managed by the users in their devices. Storing these keys requires rather expensive secure storage. To overcome that, key-aggregate cryptosystem can be used. It increases the number of networks over which the data travels. Instead of just a local area network (LAN) or storage area network (SAN), data stored on a cloud requires a WAN (wide area network) to connect them both. By sharing storage and networks with many other users/customers it is possible for other customers to access your data. Sometimes because of erroneous actions, faulty equipment, a bug and sometimes because of criminal intent. This risk applies to all types of storage and not only cloud storage. The risk of having data read during transmission can be mitigated through encryption technology. Encryption in transit protects data as it is being transmitted to and from the cloud service. Encryption at rest protects data that is stored at the service provider. Encrypting data in an on-premises cloud service on-ramp system can provide both kinds of encryption protection. There are several options available to avoid security issues. One option is to use a private cloud instead of a public cloud. Another option is to ingest data in an encrypted format where the key is held within the on-premise infrastructure. To this end, access is often by use of on-premise cloud storage gateways that have options to encrypt the data prior to transfer.

Companies are not permanent and the services and products they provide can change. Outsourcing data storage to another company needs careful investigation and nothing is ever certain. Contracts set in stone can be worthless when a company ceases to exist or its circumstances change. Companies can: Go bankrupt. Expand and change their focus. Be purchased by other larger companies. Be purchased by a company headquartered in or move to a country that negates compliance with export restrictions and thus necessitates a move. Suffer an irrecoverable disaster.

Figure 2: What Is Hybrid Cloud Security? How it Works & Best Practices Hybrid cloud storage is a term for a storage infrastructure that uses a combination of on-premises storage resources with cloud storage. The on-premises storage is usually managed by the organization, while the public cloud storage provider is responsible for the management and security of the data stored in the cloud. Hybrid cloud storage can be implemented by an on-premises cloud storage gateway that presents a file system or object storage interface that users can access in the same way they would access a local storage system. The cloud storage gateway transparently transfers the data to and from the cloud storage service, providing low latency access to the data through a local cache. Hybrid cloud storage can be used to supplement an organization's internal storage resources, or it can be used as the primary storage infrastructure. In either case, hybrid cloud storage can provide organizations with greater flexibility and scalability than traditional on-premises storage infrastructure. There are several benefits to using hybrid cloud storage, including the ability to cache frequently used data on-site for quick access, while inactive cold data is stored off-site in the cloud. This can save space, reduce storage costs and improve performance. Additionally, hybrid cloud storage can provide organizations with greater redundancy and fault tolerance, as data is stored in both on-premises and cloud storage infrastructure.

Computer data storage or digital data storage is the retention of digital data via technology consisting of computer components and recording media. Digital data storage is a core function and fundamental component of computers. Generally, the faster and volatile storage components are referred to as "memory", while slower persistent components are referred to as "storage". This distinction was extended in the Von Neumann architecture, where the central processing unit (CPU) consists of two main parts: The control unit and the arithmetic logic unit (ALU). The former controls the flow of data between the CPU and memory, while the latter performs arithmetic and logical operations on data. In practice, almost all computers use a memory hierarchy, which puts memory close to the CPU and storage further away. In modern computers, hard disk drives (HDDs) or solid-state drives (SSDs) are usually used as storage.

Impending failure on hard disk drives is estimable using S.M.A.R.T. diagnostic data that includes the hours of operation and the count of spin-ups, though its reliability is disputed. The health of optical media can be determined by measuring correctable minor errors, of which high counts signify deteriorating and/or low-quality media. Too many consecutive minor errors can lead to data corruption. Not all vendors and models of optical drives support error scanning.

Main memory (also known as primary memory, internal memory or primary storage), often referred to simply as memory, is storage directly accessible to the CPU. The CPU continuously reads instructions stored there and executes them as required. Any data actively operated on is also stored there in a uniform manner. Historically, early computers used delay lines, Williams tubes, or rotating magnetic drums as primary storage. By 1954, those unreliable methods were mostly replaced by magnetic-core memory. Core memory remained dominant until the 1970s, when advances in integrated circuit technology allowed semiconductor memory to become economically competitive. This led to modern random-access memory, which is small-sized, light, and relatively expensive. RAM used for primary storage is volatile, meaning that it loses the information when not powered for a specific time. Besides storing opened programs, it serves as disk cache and write buffer to improve both reading and writing performance. Operating systems borrow RAM capacity for caching so long as it's not needed by running software. Spare memory can be utilized as RAM drive for temporary high-speed data storage. Besides main large-capacity RAM, there are two more sub-layers of primary storage: Figure 3: What Is Hybrid Cryptography? | The Bridge to Post-Quantum Security ...

Processor registers are the fastest of all forms of data storage, being located inside the processor, with each register typically holding a word of data (often 32 or 64 bits). CPU instructions instruct the arithmetic logic unit to perform various calculations or other operations on this data. Processor cache is an intermediate stage between faster registers and slower main memory, being faster than main memory but with much less capacity. Multi-level hierarchical cache setup is also commonly used, such that primary cache is the smallest and fastest, while secondary cache is larger and slower. Primary storage, including ROM, EEPROM, NOR flash, and RAM, is usually byte-addressable. Such memory is directly or indirectly connected to the central processing unit via a memory bus, comprising an address bus and a data bus. The CPU firstly sends a number called the memory address through the address bus that indicates the desired location of data. Then it reads or writes the data in the memory cells using the data bus. Additionally, a memory management unit (MMU) is a small device between CPU and RAM recalculating the actual memory address. Memory management units allow for memory management; they may, for example, provide an abstraction of virtual memory or other tasks.

Tertiary storage or tertiary memory typically involves a robotic arm which mounts and dismount removable mass storage media from a catalog database into a storage device according to the system's demands. It is primarily used for archiving rarely accessed information, since it is much slower than secondary storage (e.g. 5–60 seconds vs. 1–10 milliseconds). This is primarily useful for extraordinarily large data stores, accessed without human operators. Typical examples include tape libraries, optical jukeboxes, and massive arrays of idle disks (MAID). Tertiary storage is also known as nearline storage because it is "near to online". Hierarchical storage management is an archiving strategy involving automatically migrating long-unused files from fast hard disk storage to libraries or jukeboxes.

Direct-attached storage (DAS) is a traditional mass storage that does not use any network. Network-attached storage (NAS) is mass storage attached to a computer that another computer can access at file level over a local area network, a private wide area network, or in the case of online file storage, over the Internet. NAS is commonly associated with the Network File System and Server Message Block protocol. Storage area network (SAN) is a specialized network that provides other computers with storage capacity. SAN is commonly associated with Fibre Channel networks.

Private clouds, for example, are used in cloud security to mitigate the increased attack surface area of outsourcing data storage. A private cloud is cloud infrastructure operated solely for a single organization, whether managed internally or by a third party, or hosted internally or externally. Hybrid cloud storage are another cloud security solution, involving storage infrastructure that uses a combination of on-premises storage resources with cloud storage. The on-premises storage is usually managed by the organization, while the public cloud storage provider is responsible for the management and security of the data stored in the cloud. Using a hybrid model allows data to be ingested in an encrypted format where the key is held within the on-premise infrastructure and can limit access to the use of on-premise cloud storage gateways, which may have options to encrypt the data prior to transfer. Cloud services are considered "public" when they are delivered over the public Internet. A virtual private cloud (VPC) is a pool of shared resources within a public cloud that provides a certain level of isolation between the different users using the resources. VPCs achieve user isolation through the allocation of a private IP subnet and a virtual communication construct (such as a VLAN or a set of encrypted communication channels) between users as well as the use of a virtual private network (VPN) per VPC user, securing, by means of authentication and encryption, the remote access of the organization to its VPC resources.

Object storage File storage Block-level storage is a concept in cloud-hosted data persistence where cloud services emulate the behaviour of a traditional block device, such as a physical hard drive, where storage is organised as blocks. Block-level storage differs from object stores or 'bucket stores' or to cloud databases. These operate at a higher level of abstraction and are able to work with entities such as files, documents, images, videos or database records. At one time, block-level storage was provided by SAN, and NAS provided file-level storage. With the shift from on-premises hosting to cloud services, this distinction has shifted. Instance stores are a form of cloud-hosted block-level storage, being provided as part of a cloud instance. Unlike other forms of block storage, instance store data will be lost the cloud instance is stopped.

Storage technologies at all levels of the storage hierarchy can be differentiated by evaluating certain core characteristics as well as measuring characteristics specific to a particular implementation. These core characteristics are: 2. System Architecture & Core Working Principles Volatility An uninterruptible power supply (UPS) can be used to give a computer a brief window of time to move information from primary volatile storage into non-volatile storage before the batteries are exhausted. Some systems, for example EMC Symmetrix, have integrated batteries that maintain volatile storage for several minutes. Mutability Storage can be classified into read/write, slow-write/fast-read (e.g. CD-RW, SSD), write-once/read-many or WORM (e.g. programmable read-only memory, CD-R), read-only storage (e.g. mask ROM ICs, CD-ROM). Accessibility Types of access include random access and sequential access. In random access, any location in storage can be accessed at any moment in approximately the same amount of time. In sequential access, the accessing of pieces of information will be in a serial order, one after the other; therefore the time to access a particular piece of information depends upon which piece of information was last accessed.

Addressability Storage can be location accessible (i.e. selected with its numerical memory address), file addressable, or content-addressable. Capacity and density Performance Storage performance metrics include latency, throughput, granularity and reliability. Energy Low capacity solid-state drives have no moving parts and consume less power than hard disks. Also, memory may use more power than hard disks. Large caches, which are used to avoid hitting the memory wall, may also consume a large amount of power. Security Semiconductor memory uses semiconductor-based integrated circuit (IC) chips to store information. Data are typically stored in metal–oxide–semiconductor (MOS) memory cells. A semiconductor memory chip may contain millions of memory cells, consisting of tiny MOS field-effect transistors (MOSFETs) and/or MOS capacitors. Both volatile and non-volatile forms of semiconductor memory exist, the former using standard MOSFETs and the latter using floating-gate MOSFETs. In modern computers, primary storage almost exclusively consists of dynamic volatile semiconductor random-access memory (RAM), particularly dynamic random-access memory (DRAM). Since the turn of the century, a type of non-volatile floating-gate semiconductor memory known as flash memory has steadily gained share as off-line storage for home computers.

Non-volatile semiconductor memory is also used for secondary storage in various advanced electronic devices and specialized computers that are designed for them. As early as 2006, notebook and desktop computer manufacturers started using flash-based solid-state drives (SSDs) as default configuration options for the secondary storage either in addition to or instead of the more traditional HDD.

Magnetic disk; Floppy disk, used for off-line storage; Hard disk drive, used for secondary storage. Magnetic tape, used for tertiary and off-line storage; Carousel memory (magnetic rolls). In early computers, magnetic storage was also used as: Microcode storage in transformer read-only storage; Primary storage in a form of magnetic memory, or core memory, core rope memory, thin-film memory and/or twistor memory; Magnetic tape was often used for secondary storage; Tertiary (e.g.

NCR CRAM) or off-line storage in the form of magnetic cards. Magnetic storage does not have a definite limit of rewriting cycles like flash storage and rewritable optical media, as altering magnetic fields causes no physical wear. Rather, their lifespan is limited by mechanical parts. CD, CD-ROM, DVD, BD-ROM: Read-only storage, used for mass distribution of digital information (music, video, computer programs); CD-R, DVD-R, DVD+R, BD-R: Write-once storage, used for tertiary and off-line storage; CD-RW, DVD-RW, DVD+RW, DVD-RAM, BD-RE: Slow write, fast read storage, used for tertiary and off-line storage; Ultra Density Optical or UDO is similar in capacity to BD-R or BD-RE and is slow-write, fast-read storage used for tertiary and off-line storage.

Magneto-optical disc storage is optical disc storage where the magnetic state on a ferromagnetic surface stores information. The information is read optically and written by combining magnetic and optical methods. Magneto-optical disc storage is non-volatile, sequential access, slow write, fast read storage used for tertiary and off-line storage. 3D optical data storage has also been proposed. Light-induced magnetization melting in magnetic photoconductors has also been proposed for high-speed, low-energy-consumption magneto-optical storage.

Aperture (computer memory) Mass storage Memory leak Memory protection Page address register Stable storage Post-quantum cryptography (PQC), sometimes referred to as quantum-proof, quantum-safe, or quantum-resistant, is the development of cryptographic algorithms (usually public-key algorithms) that are currently thought, but not proven, to be secure against a cryptanalytic attack by a quantum computer. Most widely used public-key algorithms rely on the difficulty of one of three mathematical problems: the integer factorization problem, the discrete logarithm problem, or the elliptic-curve discrete logarithm problem. All of these problems could be easily solved on a sufficiently powerful quantum computer running Shor's algorithm or possibly alternatives. As of 2026, quantum computers lack the processing power to break widely used cryptographic algorithms; however, because of the length of time required for migration to quantum-safe cryptography, cryptographers are already designing new algorithms to prepare for Y2Q or "Q-Day", the day when current algorithms will be vulnerable to quantum computing attacks. Mosca's theorem provides the risk analysis framework that helps organizations identify how quickly they need to start migrating. Their work has gained attention from academics and industry through the PQCrypto conference series hosted since 2006, several workshops on Quantum Safe Cryptography hosted by the European Telecommunications Standards Institute (ETSI), and the Institute for Quantum Computing. The rumoured existence of widespread harvest now, decrypt later programs has also been seen as a motivation for the early introduction of post-quantum algorithms, as data recorded now may still remain sensitive many years into the future. In contrast to the threat quantum computing poses to current public-key algorithms, most current symmetric cryptographic algorithms and hash functions are considered to be relatively secure against attacks by quantum computers. While the quantum Grover's algorithm does speed up attacks against symmetric ciphers, doubling the key size can effectively counteract these attacks. Thus post-quantum symmetric cryptography does not need to differ significantly from current symmetric cryptography. In 2024, the U.S. National Institute of Standards and Technology (NIST) released final versions of its first three Post-Quantum Cryptography Standards.

This approach includes cryptographic systems such as learning with errors, ring learning with errors (ring-LWE), the ring learning with errors key exchange and the ring learning with errors signature, the older NTRU or GGH encryption schemes, and the newer NTRU signature and BLISS signatures. Some of these schemes like NTRU encryption have been studied for many years without anyone finding a feasible attack. Others like the ring-LWE algorithms have proofs that their security reduces to a worst-case problem. The Post-Quantum Cryptography Study Group sponsored by the European Commission suggested that the Stehle–Steinfeld variant of NTRU be studied for standardization rather than the NTRU algorithm. At that time, NTRU was still patented. Studies have indicated that NTRU may have more secure properties than other lattice based algorithms. Two lattice-based algorithms, ML-KEM (commonly known as Kyber) and ML-DSA (commonly known as Dilithium) were among the first post-quantum algorithms standardised by NIST.

This includes cryptographic systems such as the Unbalanced Oil and Vinegar signature scheme which is based on the difficulty of solving systems of multivariate equations. Various attempts to build secure multivariate equation encryption schemes have been broken, notably the Rainbow signature. This includes cryptographic systems such as Lamport signatures, the Merkle signature scheme, the XMSS, the SPHINCS, the WOTS and the SPHINCS+ schemes. Hash based digital signatures were invented in the late 1970s by Ralph Merkle and have been studied ever since as an interesting alternative to number-theoretic digital signatures like RSA and DSA. Their primary drawback is that for any hash-based public key, there is a limit on the number of signatures that can be signed using the corresponding set of private keys. This fact reduced interest in these signatures until interest was revived due to the desire for cryptography that was resistant to attack by quantum computers. There appear to be no patents on the Merkle signature scheme and there exist many non-patented hash functions that could be used with these schemes. The stateful hash-based signature scheme XMSS developed by a team of researchers under the direction of Johannes Buchmann is described in RFC 8391. Note that all the above schemes are one-time or bounded-time signatures. Moni Naor and Moti Yung invented UOWHF hashing in 1989 and designed a signature based on hashing (the Naor-Yung scheme) which can be unlimited-time in use (the first such signature that does not require trapdoor properties).

In some versions of Ring-LWE there is a security reduction to the shortest-vector problem (SVP) in a lattice as a lower bound on the security. The SVP is known to be NP-hard. Specific ring-LWE systems that have provable security reductions include a variant of Lyubashevsky's ring-LWE signatures defined in a paper by Güneysu, Lyubashevsky, and Pöppelmann. The GLYPH signature scheme is a variant of the Güneysu, Lyubashevsky, and Pöppelmann (GLP) signature which takes into account research results that have come after the publication of the GLP signature in 2012. Another Ring-LWE signature is Ring-TESLA. There also exists a "derandomized variant" of LWE, called Learning with Rounding (LWR), which yields "improved speedup (by eliminating sampling small errors from a Gaussian-like distribution with deterministic errors) and bandwidth". While LWE uses the addition of a small error to conceal the lower bits, LWR uses rounding for the same purpose.

Unbalanced Oil and Vinegar signature schemes are asymmetric cryptographic primitives based on multivariate polynomials over a finite field I F {\displaystyle \mathbb {F} } I. Bulygin, Petzoldt, and Buchmann have shown a reduction of generic multivariate quadratic UOV systems to the NP-Hard multivariate quadratic equation solving problem. In 2005, Luis Garcia proved that there was a security reduction of Merkle Hash Tree signatures to the security of the underlying hash function. Garcia showed in his paper that if computationally one-way hash functions exist then the Merkle Hash Tree signature is provably secure. Therefore, use of a hash function with a provable reduction of security to a known hard problem would have a provable security reduction of the Merkle tree signature to that known hard problem. The Post-Quantum Cryptography Study Group sponsored by the European Commission has recommended use of the Merkle signature scheme for long term security protection against quantum computers.

The McEliece Encryption System has a security reduction to the syndrome decoding problem (SDP). The SDP is known to be NP-hard. The Post-Quantum Cryptography Study Group sponsored by the European Commission has recommended the use of this cryptography for long term protection against attack by a quantum computer. Security is related to the problem of constructing an isogeny between two supersingular curves with the same number of points. The most recent published investigation of the difficulty of this problem, by Delfs and Galbraith, indicates that this problem is as hard as the inventors of the key exchange suggest that it is. There is no security reduction to a known NP-hard problem.

A practical consideration on a choice among post-quantum cryptographic algorithms is the effort required to send public keys over the internet. From this point of view, the Ring-LWE, NTRU, and SIDH algorithms provide key sizes conveniently under 1 kB, hash-signature public keys come in under 5 kB, and MDPC-based McEliece takes about 1 kB. On the other hand, Goppa-based McEliece requires a nearly 1 MB key.

The fundamental idea of using LWE and Ring LWE for key exchange was proposed and filed at the University of Cincinnati in 2011 by Jintai Ding. The basic idea comes from the associativity of matrix multiplications, and the errors are used to provide the security. The paper appeared in 2012 after a provisional patent application was filed in 2012. In 2014, Peikert presented a key transport scheme following the same basic idea of Ding's, where the new idea of sending an additional 1 bit signal for rounding in Ding's construction is also used. For somewhat greater than 128 bits of security, Singh presents a set of parameters which have 6956-bit public keys for the Peikert's scheme. The corresponding private key would be roughly 14,000 bits. In 2015, an authenticated key exchange with provable forward security following the same basic idea of Ding's was presented at Eurocrypt 2015, which is an extension of the HMQV construction in Crypto2005. The parameters for different security levels from 80 bits to 350 bits, along with the corresponding key sizes are provided in the paper.

For 128 bits of security in NTRU, ntruhps2048509 with n = 509 and q = 2048 was selected in the NIST submission in September 2020. This results in a public key of 699 bytes and a corresponding private key of 935 bytes. 3. Software Implementation, Frameworks & Protocols In order to get 128 bits of security for hash based signatures to sign 1 million messages using the fractal Merkle tree method of Naor Shenhav and Wool the public and private key sizes are roughly 36,000 bits in length.

For 128 bits of security in a McEliece scheme, The European Commission's Post-Quantum Cryptography Study group recommends using a binary Goppa code of length at least n = 6960 and dimension at least k = 5413, and capable of correcting t = 119 errors. With these parameters the public key for the McEliece system will be a systematic generator matrix whose non-identity part takes k × (n − k) = 8373911 bits. The corresponding private key, which consists of the code support with n = 6960 elements from GF(213) and a generator polynomial of with t = 119 coefficients from GF(213), will be 92,027 bits in length. The group is also investigating the use of Quasi-cyclic MDPC codes of length at least n = 216 + 6 = 65542 and dimension at least k = 215 + 3 = 32771, and capable of correcting t = 264 errors. With these parameters the public key for the McEliece system will be the first row of a systematic generator matrix whose non-identity part takes k = 32771 bits. The private key, a quasi-cyclic parity-check matrix with d = 274 nonzero entries on a column (or twice as much on a row), takes no more than d × 16 = 4384 bits when represented as the coordinates of the nonzero entries on the first row. Barreto et al.

recommend using a binary Goppa code of length at least n = 3307 and dimension at least k = 2515, and capable of correcting t = 66 errors. With these parameters the public key for the McEliece system will be a systematic generator matrix whose non-identity part takes k × (n − k) = 1991880 bits. The corresponding private key, which consists of the code support with n = 3307 elements from GF(212) and a generator polynomial of t = 66 coefficients from GF(212), will be 40,476 bits in length.

For 128 bits of security in the supersingular isogeny Diffie–Hellman (SIDH) method, De Feo, Jao and Plut recommend using a supersingular curve modulo of a 768-bit prime. If one uses elliptic curve point compression, the public key will need to be no more than 8x768 or 6144 bits in length. A March 2016 paper by authors Azarderakhsh, Jao, Kalach, Koziel, and Leonardi showed how to cut the number of bits transmitted in half, which was further improved by authors Costello, Jao, Longa, Naehrig, Renes and Urbanik resulting in a compressed-key version of the SIDH protocol with public keys only 2640 bits in size. This makes the number of bits transmitted roughly equivalent to the non-quantum secure RSA and Diffie–Hellman at the same classical security level.

Older supported versions that have been removed because of the progression of the NIST Post-Quantum Cryptography Standardization Project are: Google has maintained the use of "hybrid encryption" in its use of post-quantum cryptography: whenever a relatively new post-quantum scheme is used, it is combined with a more proven, non-PQ scheme. This is to ensure that the data are not compromised even if the relatively new PQ algorithm turns out to be vulnerable to non-quantum attacks before Y2Q. This type of scheme is used in its 2016 and 2019 tests for post-quantum TLS, and in its 2023 FIDO2 key. One of the algorithms used in the 2019 test, SIKE, was broken in 2022, but the non-PQ X25519 layer (already used widely in TLS) still protected the data.

Apple's PQ3 and Signal's PQXDH are also hybrid. The NSA and GCHQ argue against hybrid encryption, claiming that it adds complexity to implementation and transition. Daniel J. Bernstein, who supports hybrid encryption, argues that the claims are bogus. Authors: P. Cherisma, Dr. K. Chaitanya , P. Guru Sai Charan, P. Arun Kumar, M. Venkatesh Cloud storage systems play a vital role in modern data management; however, they face significant security challenges such as unauthorized access, insecure key exchange, and lack of data integrity verification. This paper proposes a secure cloud data storage system using hybrid encryption. The system combines AES-256 GCM for efficient file encryption and RSA for secure key exchange. SHA-256 hashing is used to ensure data integrity. Additionally, role-based access control (RBAC) and activity logging mechanisms are implemented to enhance system security. Experimental results demonstrate improved confidentiality, key protection, and controlled access compared to traditional cloud storage systems.

The text presents a secure cloud storage system designed to overcome vulnerabilities in traditional cloud environments, such as weak encryption, insecure key management, lack of access control, and limited auditing. To solve these issues, the proposed system uses a hybrid encryption approach combining AES-256 GCM for fast data encryption and RSA for secure key exchange, along with SHA-256 hashing to ensure data integrity. The system introduces role-based access control with three roles: Admin, User, and Auditor, ensuring that only authorized users can access or manage data. Files are encrypted before being stored in the cloud, and AES keys are securely exchanged using RSA. Activity logging is also implemented for monitoring and auditing purposes. The architecture includes modules for authentication, encryption, decryption, file handling, access control, and logging. The workflow involves user authentication, file upload, AES encryption, RSA-based key encryption, hash generation, secure storage, and authorized decryption. The system is implemented using Python (Flask), SQLite, and standard web technologies, and its performance evaluation shows that AES provides efficient encryption even for large files while RSA ensures secure key management. Overall, the model improves security, integrity, and access control in cloud storage systems through a unified hybrid encryption framework.

The text presents a secure cloud storage system designed to overcome vulnerabilities in traditional cloud environments, such as weak encryption, insecure key management, lack of access control, and limited auditing. To solve these issues, the proposed system uses a hybrid encryption approach combining AES-256 GCM for fast data encryption and RSA for secure key exchange, along with SHA-256 hashing to ensure data integrity.

The system introduces role-based access control with three roles: Admin, User, and Auditor, ensuring that only authorized users can access or manage data. Files are encrypted before being stored in the cloud, and AES keys are securely exchanged using RSA. Activity logging is also implemented for monitoring and auditing purposes. The architecture includes modules for authentication, encryption, decryption, file handling, access control, and logging. The workflow involves user authentication, file upload, AES encryption, RSA-based key encryption, hash generation, secure storage, and authorized decryption.

The system is implemented using Python (Flask), SQLite, and standard web technologies, and its performance evaluation shows that AES provides efficient encryption even for large files while RSA ensures secure key management. Overall, the model improves security, integrity, and access control in cloud storage systems through a unified hybrid encryption framework.

The proposed system enhances cloud security by integrating AES-256 GCM and RSA for secure encryption and key exchange. SHA-256 ensures data integrity, while RBAC restricts unauthorized access. The system provides improved security and efficiency compared to traditional cloud storage systems. [1] W. Stallings, *Cryptography and Network Security: Principles and Practice*, Pearson Education. [2] B. A. Forouzan, *Cryptography and Network Security*, McGraw-Hill. [3] National Institute of Standards and Technology (NIST), “Advanced Encryption Standard (AES),” FIPS PUB 197. [4] R. Rivest, A. Shamir, and L. Adleman, “A method for obtaining digital signatures and public-key cryptosystems,” *Communications of the ACM*. [5] M. Dworkin, “Recommendation for Block Cipher Modes of Operation: Galois/Counter Mode (GCM) and GMAC,” NIST Special Publication 80038D. [6] B. S. Rawal and S. Sree Vivek, “Secure cloud storage and file sharing,” *IEEE International Conference on Smart Cloud*. [7] S. Jogarl and D. S.

Communication and Technology*, 2022. [8] G. T., S. Jayde, H. Gaurkhede, R. Vaidya, A. Wankhade, and V. Yelekar, “Secure file storage on cloud using hybrid cryptography,” *International Research Journal of Engineering and Technology*, 2021. [9] Advanced Research in Computer Science Engineering and Information Technology*, 2021. [10] A. Poduval, A. Doke, H. Sciences and Engineering*, 2019. [11] M. Malarvizhi, J. A. J. Sujana, and T. Revathi, “Secure file sharing using cryptographic techniques in cloud,” *International Conference on Green Computing Communication and Electrical Engineering*, 2014. [12] S. Burade, J. Adhikari, N. Mhaiskar, and M. Trone, “Secure file storage on cloud using hybrid cryptography with triple “Cloud secure storage mechanism based on data dispersion and encryption.” [14] S. Aslam and M. A. Shah, “Load balancing algorithms in cloud computing: A survey of modern techniques,” NSEC 2015. [15] R. Calheiros *et al.*, “CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” *Software: Standards and Technology (NIST), “Secure Hash Standard (SHS),” FIPS PUB 180-4. [18] Open Web Application Security Project (OWASP), “OWASP Top 10 Web Application Security Risks.” [19] IEEE, “Cloud computing security and privacy standards.” [20] Amazon Web Services (AWS), “AWS cloud security best practices.” [21] Microsoft Azure, “Azure security documentation.” [22] Google Clou d Platform (GCP), “Google cloud security whitepapers.” Copyright © 2026 P. Cherisma, Dr. K. Chaitanya , P. Guru Sai Charan, P. Arun Kumar, M. Venkatesh. This is an open access reproduction in any medium, provided the original work is properly cited.

Department of Computer Science and Engineering, University Institute of Technology, RGPV, Bhopal (M.P.), India Department of Computer Science and Engineering University Institute of Technology, RGPV, Bhopal (M.P.), India Abstract – The rapid expansion of cloud computing has significantly reshaped the paradigm for data management, processing, and storage. Despite the unmatched scalability, cost- effectiveness, and remote accessibility that cloud computing has to offer, this has been accompanied by an increased vulnerability to emerging security threats. The limitations that have been witnessed with the application of a single algorithm approach to cryptography have been substantial, as this approach cannot guarantee security features such as integrity, authenticity, and confidentiality at the same time. In order to fill the gap, more and more researchers have resorted to hybrid cryptographic frameworks, which leverage the benefits of different algorithms. The current paper is a structured survey of existing literature from the years 2014-2025, which focuses on hybrid frameworks using three widely used and established cryptographic mechanisms. These include the Advanced Encryption Standard (AES) algorithm, which provides rapid and reliable symmetric key data encryption, the Rivest-Shamir-Adleman (RSA) algorithm, which provides asymmetric key exchange and secure user authentication, and the Hash-based Message Authentication Code (HMAC) algorithm, which provides secure data verification and source authenticity. These three algorithms provide a comprehensive solution to the three aspects of the CIA triad, thus providing a more viable and robust security framework. The current survey includes about seventeen primary sources along with a few additional sources, which focus on real-time applications in cloud-based healthcare systems, which require additional security compliances as per HIPAA and GDPR. The current paper also focuses on emerging trends in the form of post-quantum cryptography, which is a new area of research in cloud security.

4. Practical Deployment, Analysis & Case Studies Keywords – Cloud security, AES, RSA, HMAC, hybrid cryptography, data integrity, key management, healthcare cloud systems Cloud computing has become very popular for storage, processing, and software services using scalable systems. Cloud computing enables users to access shared computing resources easily using public, private, and hybrid clouds. Due to its easy scalability, cost-effectiveness, and remote accessibility, cloud computing has been applied in many fields such as healthcare, banking, education, and business applications. However, in addition to the advantages, cloud computing platforms also introduce many security issues, particularly concerning data privacy, accuracy, and illegal access. As cloud computing platforms are normally multi-user and virtualized, confidential data is normally stored and processed using the same hardware. However, this approach also makes the data prone to various security threats like data leakage, internal threats, sharing of data between users, and loss of accuracy of the data. Further, current cloud computing infrastructures are prone to advanced threats like timing attacks, side-channel attacks, ransomware, and DDoS, which make it difficult to track and manage them through traditional security measures [34], [37], [40]. Therefore, data security has turned out to be a major issue for cloud service providers as well as cloud service users.

“The use of cryptographic methods is the basic foundation” for cloud security because it will aid in keeping data private, secure against unauthorized alterations, and properly verified. Nevertheless, the use of a particular method is normally insufficient to deal with various security threats. All three of these techniques are only concerned with securing only one of the three major characteristics of data: CIA. AES is for data confidentiality; thus, they cannot individually address all three of the security requirements for cloud computing. This implies the use of hybrid techniques instead. As a hybrid technique, HMAC is generally utilized for data integrity in clouds; likewise, encryption of the data in the cloud gets carried out by using the hybrid form of AES [1],[4],[6],[11].

Studies have found that the integration of AES, RSA, and HMAC improves cloud security by blocking leaks, securing access, and maintaining efficiency. These techniques are widely applied in various sectors, particularly in healthcare cloud systems that comply with HIPAA, GDPR, and other standards to properly encrypt, verify, and manage data [4], [6], [11]. This research will explore the hybrid cryptography process involving AES, RSA, and HMAC, particularly from 2014 to 2025. It will also point out the gaps to highlight the latest trends in advanced cryptography, such as post-quantum cryptography.

A hybrid cryptography technique that involves a combination of AES, RSA, and HMAC has been one of the most prominent things in the last ten years to secure cloud infrastructure. These hybrid models were developed because the use of a single technique for encryption suffers a lot of limitations, and it is not good enough to provide complete security. These hybrid models aimed at solving the issues of data security, data integrity, and user authentication simultaneously. The research studies between 2014 and 2025 depict a gradual enhancement of the hybrid cryptographic models. This is due to growing cloud technology adoption, entailing strict security policies, as well as sophisticated cyber- attacks.

Research works that were conducted on the subject of hybrid cryptography were mainly centered on the integration of symmetric and symmetric encryption for the purpose of protecting information that is storied in the cloud. One of the first hybrid methods for the security of cloud data. In the study done by Shahade and Mahalle [1] published in , AES-128 is used as the encryption technique for the data of the users. In the same study, the secret key used for the AES is encrypted using the RSA technique with a key size of 1024. The study is done using the EyeOS technique. The results showed that the data can be securely done without the cloud administrators being able to view the data. Also, the study is done with a very less amount of data.

Singh et al. [19] proposed the use of the hybrid approach to secure the data in the field of telemedicine and remote patient monitoring services. In the proposed approach of Singh et al., the feature of AES was used to secure the medical data being received through sensors. As cloud systems started to be adopted in regulated environments, there was a growing realization that many cloud vendors environments, researchers began to focus on rules, policies, and domain-based security needs. A hybrid encryption scheme for cloud-based health care. The systems proposed by Khattak et al. [2] used AES for encrypting data and RSA for easy management of keys. “primarily to comply with HIPAA regulations.” The findings of their research indicated that using effective cryptography is highly significant to ensure patient privacy and data accessibility in cloud setups.. However, their research primarily concentrated on policies and system design rather than evaluating speed and efficiency, and hence, the efficiency aspect was not explored.

Sharma and Rohini [5] introduced data integrity verification into the hybrid cloud security framework by employing the use of HMAC with SHA-512, in addition to AES and RSA encryption algorithms. The algorithm demonstrated that HMAC is capable of detecting any unauthorized modifications to the data with very little overhead cost compared to re-encryption algorithms. However, the authors failed to demonstrate how the HMAC keys are to be shared securely.

Bansal and Kaushik [18] proposed an improved model by utilizing AES-256 with RSA-2048 for modular cloud encryption, which showed better performance of about 35-40% compared to systems that used only RSA encryption. But since their model did not have a proper integrity check mechanism, the entire security of the system was not addressed. With the increasing size of data and cloud computing, scientists began to pay more attention to improving the speed of hybrid encryption systems. A hybrid encryption environment proposed by Patel et al. [3] utilized parallel processing to reduce the encryption time required in large cloud data centers. The result of their research showed that multi-thread encryption can significantly reduce delays, although the performance analysis was not very elaborate.

Kumar and Kumar [12] made a thorough analysis of various key sizes of RSA (1024, 2048, and 4096 bits) in AES- RSA hybrid cryptosystems. They concluded that a 2048-bit key provides the optimal combination of high security and high speed, and larger keys increase delays without providing much additional security. Nair and Binu [14]. proposed the application of hybrid cryptography in industrial IoT communication systems They demonstrated that hybrid encryption can also be applied in real- time systems by introducing delays of less than 20 milliseconds in their approach, which combines AES and RSA at the application level. During this time, hybrid encryption schemes evolved from basic concepts to more performance-oriented schemes that are capable of handling large and time-critical applications.

The later studies were mainly focused on data integrity, auditing and trust verification within clouds environments. A hybrid AES-RSA-HMAC system with homomorphic property hashing was proposed by Kumar in [6]. This made it possible for third-party auditors to verify the integrity of “the data without access to the data itself.” The However, results 5. Performance Evaluation & Extended Technical Observations revealed improved speed performance compared to by the RSA system alone, with integrity being maintained. It is therefore appropriate in applications such as healthcare and in the government sector, where regulations are stringent.

Akter et al. [8] studied and tested the performance of AES- RSA-HMAC using large data sets. concluded that the addition of HMAC processing without increasing the processing burden by any greater percentage than 2%. This was done to maintain the security of the information being exchanged between the perform almost as well as AES-only systems, while still providing improved key management and integrity verification.

In the paper by Naik et al. [9], [32], the authors tackle the important issue of overlooked in cloud security by employing encryption for communication in cloud-based intrusion detection systems. The authors work guaranteed improved security for IDS messages using RSA, thereby protecting against spoofing attacks and DDoS attacks. This work demonstrated the applicability of hybrid cryptography not in data storage alone, but also in data communication.

Lee [34] gave a broad survey of hybrid encryption schemes and classified them into data-oriented, communication- oriented, and integrity-oriented systems. The classification not only explained the usage of hybrid cryptographic schemes but also revealed that AES-RSA-HMAC is the most popular combination in cloud security research. In the upcoming 2021-25 cycle, the realm of the healthcare industry, the multi-cloud space, and the enterprise applications are where the above-mentioned hybrid cryptography meets reality.

Ranganathan and Srinivasan’s [11] use of AES, RSA, and HMAC-SHA3 to protect the integrity of data systems in health care facilities generated nearly zero false warnings during the integrity test process. This confirms that integrity verification is quite useful for protecting patients data and ensuring their security. Durge and Deshmukh [4], [24] also examined the available hybrid encrypt models for the setup. Durge and Deshmukh focused on the secure transfer and synchronization of information within the various setups. The researchers noted good compatibility along with a small performance expense.

Bharti and Singh [10] have used the concept of combining hybrid encryption with role-based access control and multi- factor authentications to reduce the risk of insider attacks on enterprise clouds. The cryptography results observed the significance of proper access control along. Fathima [49] proposed the application of the blockchain- based framework of AES RSA in order to ensure that where every encryption is written on the distributed ledger.Although this makes auditing easier, many encryption The overall system is also made more complicated by processes. We discuss here works published in English, first starting with those that assume symmetric-key primitives.

Patel [58] presented a power-efficient AESRSA hybrid. scheme, which improves key management, employs performs all the major phases of asynchronous encryption efficiently and leverages hardware. more effectively. The results present around 25% power of saving vis-vis traditional hybrid architectures without compromising security. Chauhan [16] reviewed RSA defenses and found that That capable quantum attackers could break RSA-based key exchange.

It followed the work that advocated moving compared to lattice-based schemes such as Kyber and NTRU while continuing to use AES and HMAC, correspondinly for the data. encryption and integrity. Bhattacharya and Mehta [15] developed an improved variant of RSA, called XRSA, that lessens the risk due vulnerable to side-channel attacks and providing an efficient key encapsulation. Under the hybrid configuration, it reduced the key-wrapping time by roughly 25%, which is helpful for large cloud settings where key exchange frequency is Higher.

Patel and Chauhan [17] through the insertion of SHA-256 hashing before RSA encryption. This approach reduced the timing discrepancies in encryption and benefited cloud APIs and real-time services, but had extra computational overhead as a trade-off, underscoring the security/performance trade-off again. Verma and Gupta [33] developed a three-layer hybrid security model that uses the integration of AES, RSA, and SHA-512 for security of enterprise cloud files. The experimental result achieved perfect accuracy in tamper detection (100%). However, this work focuses much on enterprise usage and does not address multi-cloud or cross- domain scalability.

From 2014 to 2025, as far as the trend of hybrid cryptography for cloud security is concerned, it has made consistent and steady progress. The initial works mainly combined AES and RSA to ensure confidentiality and secure key exchange of keys; thus, cryptographic hybrids are applicable to cloud security. Nevertheless, some of the pioneering works have overlooked certain robust integrity checks and have not fully considered the scalability factor.

Naturally, as the years have continued, improvement has been made to HMAC, which has increased its ability to protect against tampering while maintaining a small cost in system performance. Finally, as the industry has matured, speed, parallelism, as well as real-time usage, have become areas of interest, such as in healthcare, intrusion detection, cloud computing, etc.

Harvested Online Documentation & Reference Sources • https://en.wikipedia.org/wiki/Cloud%20storage • https://en.wikipedia.org/wiki/Computer%20data%20storage • https://en.wikipedia.org/wiki/Post-quantum%20cryptography • https://www.ijraset.com/research-paper/secure-cloud-data-storage-using-hybrid-encryption • https://www.ijert.org/enhancing-cloud-security-through-hybrid-cryptography-a-comprehensive-survey-of-aes-rsa-and-hmac-b ased-techniques-ijertv15is031138