Biblio

From the past few years cloud services are so popular and are being used by many people from various domains for various purposes such as data storage, e-mails, backing up data and much more. While there were many options to perform such things why did people choose cloud? The answer is clouds are more flexible, convenient, reliable and efficient. Coming to security of data over cloud, it is secure to store data over cloud rather than storing data locally as there is chance of some computer breakdown or any natural disaster may also occur. There are also many threats for data security over cloud namely data breaching, lack of access-key management and much more. As the data has been processed and being stored online for various purposes, there is a clear requirement for data security. Many organizations face various challenges while storing their data over cloud such as data leakages, account hijacking, insufficient credentials and so on. So to overcome these challenges and safeguard the data, various encryption techniques were implemented. However, even though encryption is used, the data still needs to be decrypted in order to do any type of operation. As a result, we must choose a manner in which the data can be analyzed, searched for, or used in any other way without needing to be decoded. So, the objective is to introduce a technique that goes right for the above conditions mentioned and for data security over cloud.

The student's fault is not the only cause of dropping out of school. Often, cases of dropping out of school are only associated with too general problems. However, sensitive issues that can be detrimental to certain parties in this regard, such as the institution's reputation, are usually not made public. To overcome this, an in-depth analysis of these cases is needed for proper handling. Many risks are associated with creating a single repository for this sensitive information. Therefore, some encryption is required to ensure data is not leaked. However, encryption at rest and in transit is insufficient as data leakage is a considerable risk during processing. In addition, there is also a risk of abuse of authority by insiders so that no single entity is allowed to have access to all data. Homomorphic encryption presents a viable solution to this challenge. Data may be aggregated under the security provided by Homomorphic Encryption. This method makes the data available for computation without being decrypted first and without paying the risk of having a single repository.

In 2020, Omar and abed proposed a new noise-free fully homomorphic encryption scheme that allows arbitrary computations on encrypted data without decryption. However, they did not provide a sufficient security analysis of the proposed scheme and just stated that it is secure under the integer factorization assumption. In this paper, we present known plaintext attacks on their scheme and illustrate them with toy examples. Our attack algorithms are quite simple: They require several times of greatest common divisor (GCD) computations using only a few pair of message and ciphertext.

Cloud computing has been widely used because of its low price, high reliability, and generality of services. However, considering that cloud computing transactions between users and service providers are usually asynchronous, data privacy involving users and service providers may lead to a crisis of trust, which in turn hinders the expansion of cloud computing applications. In this paper, we propose DPP, a data privacy-preserving cloud computing scheme based on homomorphic encryption, which achieves correctness, compatibility, and security. DPP implements data privacy-preserving by introducing homomorphic encryption. To verify the security of DPP, we instantiate DPP based on the Paillier homomorphic encryption scheme and evaluate the performance. The experiment results show that the time-consuming of the key steps in the DPP scheme is reasonable and acceptable.

Homomorphic encryption (HE) facilitates computing over encrypted data without using the secret keys. It is currently inefficient for practical implementation on the Internet of Things (IoT). However, the performance of these HE schemes may increase with optimized libraries and hardware capabilities. Thus, implementing and analyzing HE schemes and protocols on resource-constrained devices is essential to deriving optimized and secure schemes. This paper develops an energy profiling framework for homomorphic encryption on IoT devices. In particular, we analyze energy consumption and performance such as CPU and Memory utilization and execution time of numerous HE schemes using SEAL and HElib libraries on the Raspberry Pi 4 hardware platform and study energy-performance-security trade-offs. Our analysis reveals that HE schemes can incur a maximum of 70.07% in terms of energy consumption among the libraries. Finally, we provide guidelines for optimization of Homomorphic Encryption by leveraging multi-threading and edge computing capabilities for IoT applications. The insights obtained from this study can be used to develop secure and resource-constrained implementation of Homomorphic encryption depending on the needs of IoT applications.

With the development of sensor technology, people put forward a higher level, more diversified demand for portable rangefinders. However, its data storage method has not been developed in a large scale and breakthrough. This paper studies the design of portable sensor data storage system based on homomorphic encryption algorithm, which aims to maintain the security of sensor data storage through homomorphic encryption algorithm. This paper analyzes the functional requirements of the sensor data storage system, puts forward the overall design scheme of the system, and explains in detail the requirements and indicators for the specific realization of each part of the function. Analyze the different technical resources currently used in the storage system field, and dig deep into the key technologies that match the portable sensor data storage system. This paper has changed the problem of cumbersome operation steps and inconvenient data recovery in the sensor data storage system. This paper mainly uses the method of control variables and data comparison to carry out the experiment. The experimental results show that the success rate of the sensor data storage system under the homomorphic encryption algorithm is infinitely close to 100% as the number of data blocks increases.

This paper presents topological sorting methods to minimize the multiplicative depth of encrypted arithmetic expressions. The research aims to increase compatibility between nonlinear dynamic control schemes and homomorphic encryption methods, which are known to be limited by the quantity of multiplicative operations. The proposed method adapts rewrite rules originally developed for encrypted binary circuits to depth manipulation of arithmetic circuits. The paper further introduces methods to normalize circuit paths that have incompatible depth. Finally, the paper provides benchmarks demonstrating the improved depth in encrypted computed torque control of a dynamic manipulator and discusses how achieved improvements translate to increased cybersecurity.

Cloud computing plays major role in the development of accessing clouduser’s document and sensitive information stored. It has variety of content and representation. Cyber security and attacks in the cloud is a challenging aspect. Information security attains a vital part in Cyber Security management. It involves actions intended to reduce the adverse impacts of such incidents. To access the documents stored in cloud safely and securely, access control will be introduced based on cloud users to access the user’s document in the cloud. To achieve this, it is highly required to combine security components (e.g., Access Control, Usage Control) in the security document to get automatic information. This research work has proposed a Role Key Homomorphic Encryption Algorithm (RKHEA) to monitor the cloud users, who access the services continuously. This method provides access creation of session-based key to store the singularized encryption to reduce the key size from random methods to occupy memory space. It has some terms and conditions to be followed by the cloud users and also has encryption method to secure the document content. Hence the documents are encrypted with the RKHEA algorithm based on Service Key Access (SKA). Then, the encrypted key will be created based on access control conditions. The proposed analytics result shows an enhanced control over the documents in cloud and improved security performance.

Fully homomorphic encryption technologies allow you to operate on encrypted data without disclosing it, therefore they have a lot of potential for solving personal data storage and processing issues. Because of the increased interest in these technologies, various software tools and libraries that allow completely homomorphic encryption have emerged. However, because this subject of cryptography is still in its early stages, standards and recommendations for the usage of completely homomorphic encryption algorithms are still being developed. The paper presents the main areas of application of homomorphic encryption. The analysis of existing developments in the field of homomorphic encryption is carried out. The analysis showed that existing library implementations do not support the division and subtraction operation. The analysis revealed the need to develop a library of fully homomorphic encryption, which allows performing all mathematical operations on them (addition, difference, multiplication and division), as well as the relevance of developing its own implementation of a library of homomorphic encryption on integers. Then, implement the development of a fully homomorphic encryption library in C++ and on an ESP 32 microcontroller. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the scope of application of homomorphic encryption. A method of homomorphic division and subtraction is proposed that allows performing the division and subtraction operation on homomorphically encrypted data. The level of security, the types of operations executed, the maximum length of operands, and the algorithm's running time are all described as a consequence of numerical experimentation with parameters.

Homomorphic encryption is suitable for a machine learning in the cloud such as a privacy-preserving machine learning. However, ordinary homomorphic public key encryption has a problem that public key holders can generate ciphertexts and anyone can execute homomorphic operations. In this paper, we will propose a solution based on the Keyed Homomorphic-Public Key Encryption proposed by Emura et al.

Data privacy is a key concern for smart contracts handling sensitive data. The existing work zkay addresses this concern by allowing developers without cryptographic expertise to enforce data privacy. However, while zkay avoids fundamental limitations of other private smart contract systems, it cannot express key applications that involve operations on foreign data.We present ZeeStar, a language and compiler allowing non-experts to instantiate private smart contracts and supporting operations on foreign data. The ZeeStar language allows developers to ergonomically specify privacy constraints using zkay’s privacy annotations. The ZeeStar compiler then provably realizes these constraints by combining non-interactive zero-knowledge proofs and additively homomorphic encryption.We implemented ZeeStar for the public blockchain Ethereum. We demonstrated its expressiveness by encoding 12 example contracts, including oblivious transfer and a private payment system like Zether. ZeeStar is practical: it prepares transactions for our contracts in at most 54.7s, at an average cost of 339k gas.

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.

Data security has become the most important issue in every institution or company. With the existence of hackers, intruders, and third parties on the cloud, securing data has become more challenging. This paper uses a hybrid encryption method that is based on the Elliptic Curve Cryptography (ECC) and Fully Homomorphic Encryption (FHE). ECC is used as a lightweight encryption algorithm that can provide a good level of security. Besides, FHE is used to enable data computation on the encrypted data in the cloud. In this paper, the concept of unique values is combined with the hybrid encryption method. Using the concept of unique values contributes to decreasing the encryption and decryption time obviously. To evaluate the performance of the combined encryption method, the provided results are compared with the ones in the encryption method without using the concept of unique values. Experiments show that the combined encryption method can reduce the encryption time up to 43% and the decryption time up to 56%.

Ensuring data rights, openness and transaction flow is important in today’s digital economy. Few scholars have studied in the area of data confirmation, it is only with the development of blockchain that it has started to be taken seriously. However, blockchain has open and transparent natures, so there exists a certain probability of exposing the privacy of data owners. Therefore, in this paper we propose a new measure of data confirmation based on Ciphertext-Policy Attribute-Base Encryption(CP-ABE). The information with unique identification of the data owner is embedded in the ciphertext of CP-ABE by paillier homomorphic encryption, and the data can have multiple sharers. No one has access to the plaintext during the whole confirmation process, which reduces the risk of source data leakage.

Efficient large-scale biometric identification is a challenging open problem in biometrics today. Adding biometric information protection by cryptographic techniques increases the computational workload even further. Therefore, this paper proposes an efficient and improved use of coefficient packing for homomorphically protected biometric templates, allowing for the evaluation of multiple biometric comparisons at the cost of one. In combination with feature dimensionality reduction, the proposed technique facilitates a quadratic computational workload reduction for biometric identification, while long-term protection of the sensitive biometric data is maintained throughout the system. In previous works on using coefficient packing, only a linear speed-up was reported. In an experimental evaluation on a public face database, efficient identification in the encrypted domain is achieved on off-the-shelf hardware with no loss in recognition performance. In particular, the proposed improved use of coefficient packing allows for a computational workload reduction down to 1.6% of a conventional homomorphically protected identification system without improved packing.

Considered sensitive information by the ISO/IEC 24745, biometric data should be stored and used in a protected way. If not, privacy and security of end-users can be compromised. Also, the advent of quantum computers demands quantum-resistant solutions. This work proposes the use of Kyber and Saber public key encryption (PKE) algorithms together with homomorphic encryption (HE) in a face recognition system. Kyber and Saber, both based on lattice cryptography, were two finalists of the third round of NIST post-quantum cryptography standardization process. After the third round was completed, Kyber was selected as the PKE algorithm to be standardized. Experimental results show that recognition performance of the non-protected face recognition system is preserved with the protection, achieving smaller sizes of protected templates and keys, and shorter execution times than other HE schemes reported in literature that employ lattices. The parameter sets considered achieve security levels of 128, 192 and 256 bits.

Biometric security is the fastest growing area that receives considerable attention over the past few years. Digital hiding and encryption technologies provide an effective solution to secure biometric information from intentional or accidental attacks. Visual cryptography is the approach utilized for encrypting the information which is in the form of visual information for example images. Meanwhile, the biometric template stored in the databases are generally in the form of images, the visual cryptography could be employed effectively for encrypting the template from the attack. This study develops a share creation with improved encryption process for secure biometric verification (SCIEP-SBV) technique. The presented SCIEP-SBV technique majorly aims to attain security via encryption and share creation (SC) procedure. Firstly, the biometric images undergo SC process to produce several shares. For encryption process, homomorphic encryption (HE) technique is utilized in this work. To further improve the secrecy, an improved bald eagle search (IBES) approach was exploited in this work. The simulation values of the SCIEP-SBV system are tested on biometric images. The extensive comparison study demonstrated the improved outcomes of the SCIEP-SBV technique over compared methods.

Cooperative secure computing based on the relationship between numerical value and numerical interval is not only the basic problems of secure multiparty computing but also the core problems of cooperative secure computing. It is of substantial theoretical and practical significance for information security in relation to scientific computing to continuously investigate and construct solutions to such problems. Based on the Goldwasser-Micali homomorphic encryption scheme, this paper propose the Morton rule, according to the characteristics of the interval, a double-length vector is constructed to participate in the exclusive-or operation, and an efficient cooperative decision-making solution for integer and integer interval security is designed. This solution can solve more basic problems in cooperative security computation after suitable transformations. A theoretical analysis shows that this solution is safe and efficient. Finally, applications that are based on these protocols are presented.

In an advanced metering infrastructure (AMI), the electric utility collects power consumption data from smart meters to improve energy optimization and provides detailed information on power consumption to electric utility customers. However, AMI is vulnerable to data falsification attacks, which organized adversaries can launch. Such attacks can be detected by analyzing customers' fine-grained power consumption data; however, analyzing customers' private data violates the customers' privacy. Although homomorphic encryption-based schemes have been proposed to tackle the problem, the disadvantage is a long execution time. This paper proposes a new privacy-preserving data falsification detection scheme to shorten the execution time. We adopt elliptic curve cryptography (ECC) based on homomorphic encryption (HE) without revealing customer power consumption data. HE is a form of encryption that permits users to perform computations on the encrypted data without decryption. Through ECC, we can achieve light computation. Our experimental evaluation showed that our proposed scheme successfully achieved 18 times faster than the CKKS scheme, a common HE scheme.

Ensuring sustainable sourcing of crude materials and production of goods is a pressing problem in consideration of the growing world population and rapid climate change. Supply-chain traceability systems based on distributed ledgers can help to enforce sustainability policies like production limits. We propose two mutually independent distributed-ledger-based protocols that enable public verifiability of policy compliance. They are designed for different supply-chain scenarios and use different privacy-enhancing technologies in order to protect confidential supply-chain data: secret sharing and homomorphic encryption. The protocols can be added to existing supply-chain traceability solutions with minor effort. They ensure confidentiality of transaction details and offer public verifiability of producers' compliance, enabling institutions and even end consumers to evaluate sustainability of supply chains. Through extensive theoretical and empirical evaluation, we show that both protocols perform verification for lifelike supply-chain scenarios in perfectly practical time.

With the popularization of mobile communication and sensing equipment, as well as the rapid development of location-aware technology and wireless communication technology, LBSs(Location-based services) bring convenience to people’s lives and enable people to arrange activities more efficiently and reasonably. It can provide more flexible LBS proximity detection query, which has attracted widespread attention in recent years. However, the development of proximity detection query still faces many severe challenges including query information privacy. For example, when users want to ensure their location privacy and data security, they can get more secure location-based services. In this article, we propose an efficient and privacy-protecting proximity detection framework based on location services: PD(Proximity Detection). Through PD, users can query the range of arbitrary polygons and obtain accurate LBS results. Specifically, based on homomorphic encryption technology, an efficient PRQ(polygon range query) algorithm is constructed. With the help of PRQ, PD, you can obtain accurate polygon range query results through the encryption request and the services provided by the LAS(LBS Agent Server) and the CS(Cloud Server). In addition, the query privacy of the queryer and the information of the data provider are protected. The correctness proof and performance analysis show that the scheme is safe and feasible. Therefore, our scheme is suitable for many practical applications.

Security is a most concerning matter for client's data in today's emerging technological world in each field, like banking, management, retail, shopping, communication, education, etc. Arise in cyber-crime due to the black hat community, there is always a need for a better way to secure the client's sensitive information, Security is the key point in online banking as the threat of unapproved online access to a client's data is very significant as it ultimately danger to bank reputation. The more secure and powerful methods can allow a client to work with untrusted parties. Paper is focusing on how secure banking transaction system can work by using homomorphic encryption and steganography techniques. For data encryption NTRU, homomorphic encryption can be used and to hide details through the QR code, a cover image can be embed using steganography techniques.

We suggest using Fully Homomorphic Encryption (FHE) to be used, not only to keep the privacy of information but also, to verify computations with no additional significant overhead, using only part of the variables length for verification. This method supports the addition of encrypted values as well as multiplication of encrypted values by the addition of their logarithmic representations and is based on a separation between hardware functionalities. The computer/server performs blackbox additions and is based on the separation of server/device/hardware, such as the enclave, that may deal with additions of logarithmic values and exponentiation. The main idea is to restrict the computer operations and to use part of the variable for computation verification (computation fingerprints) and the other for the actual calculation. The verification part holds the FHE value, of which the calculated result is known (either due to computing locally once or from previously verified computations) and will be checked against the returned FHE value. We prove that a server with bit computation granularity can return consistent encrypted wrong results even when the public key is not provided. For the case of computer word granularity the verification and the actual calculation parts are separated, the verification part (the consecutive bits from the LSB to the MSB of the variables) is fixed across all input vectors. We also consider the case of Single Instruction Multiple Data (SIMD) where the computation fingerprints index in the input vectors is fixed across all vectors.

This paper proposes a configurable architecture of butterfly unit (BU) supporting number theoretic transform (NTT) and inverse NTT (INTT) accelerators in the ring learning with error based homomorphic encryption. The proposed architecture is fully pipelined and carefully optimized the critical path delay. To compare with related works, several BU designs of different bit-size specific primes are synthesized and successfully placed-and-routed on the Xilinx Zynq UltraScale+ ZCU102 FPGA platform. Implementation results show that the proposed BU designs achieve 3× acceleration with more efficient resource utilization compared with previous works. Thus, the proposed BU architecture is worthwhile to develop NTTINTT accelerators in advanced homomorphic encryption systems.

The article proposes a general approach to the implementation of encryption schemes based on the group of automorphisms of the Hermitian functional field. The three-parameter group is used with logarithmic captions outside the center of the group. This time we applied for an encryption scheme based on a Hermitian function field with homomorphic encryption. The use of homomorphic encryption is an advantage of this implementation. The complexity of the attack and the size of the encrypted message depends on the strength of the group.