Biblio

Homomorphic encryption enables computation on encrypted data while maintaining secrecy. This leads to an important open question whether quantum computation can be delegated and verified in a non-interactive manner or not. In this paper, we affirmatively answer this question by constructing the quantum homomorphic encryption scheme with quantum obfuscation. It takes advantage of the interchangeability of the unitary operator, and exchanges the evaluation operator and the encryption operator by means of equivalent multiplication to complete homomorphic encryption. The correctness of the proposed scheme is proved theoretically. The evaluator does not know the decryption key and does not require a regular interaction with a user. Because of key transmission after quantum obfuscation, the encrypting party and the decrypting party can be different users. The output state has the property of complete mixture, which guarantees the scheme security. Moreover, the security level of the quantum homomorphic encryption scheme depends on quantum obfuscation and encryption operators.

The linear regression is a machine learning algorithm used for prediction. But if the input data is in plaintext form then there is a high probability that the sensitive information will get leaked. To overcome this, here we are proposing a method where the input data is encrypted using Homomorphic encryption. The machine learning algorithm can be used on this encrypted data for prediction while maintaining the privacy and secrecy of the sensitive data. The output from this model will be an encrypted result. This encrypted result will be decrypted using a Homomorphic decryption technique to get the plain text. To determine the accuracy of our result, we will compare it with the result obtained after applying the linear regression algorithm on the plain text.

With the shift in storage paradigm, there is an increasing need for privacy of dataset and also for an encryption scheme that permits computation on encrypted data. Paillier cryptosystem is a good example of such a homomorphic encryption scheme. To improve the efficiency of the Paillier homomorphic encryption scheme in terms of its decryption speed and overall computational cost, we propose an improved decryption process. Specifically, the inclusion of a variable k to reduce the modular multiplicative arithmetic. The variable k is combined with the L function and CRT recombination method, to arrive at a fast and improved decryption process, showing the mathematical correctness of the decryption algorithm. Experimental results validate that our scheme is significantly efficient in its decryption speed.

Fully homomorphic encryption (FHE) was one of the most prominent research topics of the last ten years. And it is considered as a major cryptographic tool in a secure and reliable cloud computing environment. The reason behind that because it allows computations over encrypted data, without decrypting the original message. This paper developed a new symmetric (FHE) algorithm based on Enhanced Matrix Operation for Randomization and Encryption (EMORE) algorithm using a chaotic system. The proposed algorithm was considered a noise-free algorithm. It generates the ciphertext in a floating-point number's format, overcomes the problem of plaintext ring and modular arithmetic operation in EMORE by the hardness of a chaotic system, and provides another level of security in terms of randomness properties, sensitivity to the initial condition, and large key size (\textbackslashtextgreater2100) of a chaotic system. Besides that, the proposed algorithm provides the confidentiality and privacy of outsourced data computing through homomorphism property of it. By using both numerical and statistical tests, these tests proved that the proposed algorithm has positive randomness properties and provide secure and reliable encryption (through encryption-decryption time, key sensitivity, keyspace, and correlation). Finally, the simulation results show that the execution time of the proposed algorithm is faster about 7.85 times than the EMORE algorithm.

This is a feasibility study on homomorphic encryption using the MK-TFHE library in daily computing using cloud services. Logic gates OR, AND, XOR, XNOR, NOR were created. A basic set of arithmetic operations namely - addition, subtraction, multiplication and division were also created. This research is a continuation of a previous work and this peeks into the newly created logic gates on these arithmetic operations.

In recent days the attention of the researchers has been grabbed by the advent of fog computing which is found to be a conservatory of cloud computing. The fog computing is found to be more advantageous and it solves mighty issues of the cloud namely higher delay and also no proper mobility awareness and location related awareness are found in the cloud environment. The IoT devices are connected to the fog nodes which support the cloud services to accumulate and process a component of data. The presence of Fog nodes not only reduces the demands of processing data, but it had improved the quality of service in real time scenarios. Nevertheless the fog node endures from challenges of false data injection, privacy violation in IoT devices and violating integrity of data. This paper is going to address the key issues related to homomorphic encryption algorithms which is used by various researchers for providing data integrity and authenticity of the devices with their merits and demerits.

This study proposes a machine learning method based on stream homomorphic encryption computing for improving security and reducing computational time. A case study of mobile positioning based on k nearest neighbors ( kNN) is selected to evaluate the proposed method. The results showed the proposed method can save computational resources than others.

Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.

Various research efforts have focused on the problem of customer privacy protection in the smart grid arising from the large deployment of smart energy meters. In fact, the deployed smart meters distribute accurate profiles of home energy use, which can reflect the consumers' behaviour. This paper proposes a privacy-preserving lattice-based homomorphic aggregation scheme. In this approach, the smart household appliances perform the data aggregation while the smart meter works as relay node. Its role is to authenticate the exchanged messages between the home area network appliances and the related gateway. Security analysis show that our scheme guarantees consumer privacy and messages confidentiality and integrity in addition to its robustness against several attacks. Experimental results demonstrate the efficiency of our proposed approach in terms of communication complexity.

In recent years, service providers, such as mobile operators providing wireless services, collected location data in enormous extent with the increase of the usages of mobile phones. Vertical businesses, such as banks, may want to use this location information for their own scenarios. However, service providers cannot directly provide these private data to the vertical businesses because of the privacy and legal issues. In this demo, we show how privacy preserving solutions can be utilized using such location-based queries without revealing each organization's sensitive data. In our demonstration, we used partially homomorphic cryptosystem in our protocols and showed practicality and feasibility of our proposed solution.

Benchmarking is an important measure for companies to investigate their performance and to increase efficiency. As companies usually are reluctant to provide their key performance indicators (KPIs) for public benchmarks, privacy-preserving benchmarking systems are required. In this paper, we present an enhanced privacy-preserving benchmarking protocol, which we implemented and evaluated based on the real-world scenario of product cost optimisation. It is based on homomorphic encryption and enables cloud-based KPI comparison, providing a variety of statistical measures. The theoretical and empirical evaluation of our benchmarking system underlines its practicability.

When employing biometric recognition systems, we have to take into account that biometric data are considered sensitive data. This has raised some privacy issues, and therefore secure systems providing template protection are required. Using homomorphic encryption, permanent protection can be ensured, since templates are stored and compared in the encrypted domain. In addition, the unprotected system's accuracy is preserved. To solve the problem of the computational overload linked to the encryption scheme, we present an early decision making strategy for iris-codes. In order to improve the recognition accuracy, the most consistent bits of the iris-code are moved to the beginning of the template. This allows an accurate block-wise comparison, thereby reducing the execution time. Hence, the resulting system grants template protection in a computationally efficient way. More specifically, in the experimental evaluation in identification mode, the block-wise comparison achieves a 92% speed-up on the IITD database with 300 enrolled templates.

Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data.

GPS signals, the main origin of navigation, are not functional in indoor environments. Therefore, Wi-Fi access points have started to be increasingly used for localization and tracking inside the buildings by relying on fingerprint-based approach. However, with these types of approaches, several concerns regarding the privacy of the users have arisen. Malicious individuals can determine a clients daily habits and activities by simply analyzing their wireless signals. While there are already efforts to incorporate privacy to the existing fingerprint-based approaches, they are limited to the characteristics of the homo-morphic cryptographic schemes they employed. In this paper, we propose to enhance the performance of these approaches by exploiting another homomorphic algorithm, namely DGK, with its unique encrypted sorting capability and thus pushing most of the computations to the server side. We developed an Android app and tested our system within a Columbia University dormitory. Compared to existing systems, the results indicated that more power savings can be achieved at the client side and DGK can be a viable option with more powerful server computation capabilities.

Machine Learning as a Service (MLaaS) is becoming a popular practice where Service Consumers, e.g., end-users, send their data to a ML Service and receive the prediction outputs. However, the emerging usage of MLaaS has raised severe privacy concerns about users' proprietary data. PrivacyPreserving Machine Learning (PPML) techniques aim to incorporate cryptographic primitives such as Homomorphic Encryption (HE) and Multi-Party Computation (MPC) into ML services to address privacy concerns from a technology standpoint. Existing PPML solutions have not been widely adopted in practice due to their assumed high overhead and integration difficulty within various ML front-end frameworks as well as hardware backends. In this work, we propose PlaidML-HE, the first end-toend HE compiler for PPML inference. Leveraging the capability of Domain-Specific Languages, PlaidML-HE enables automated generation of HE kernels across diverse types of devices. We evaluate the performance of PlaidML-HE on different ML kernels and demonstrate that PlaidML-HE greatly reduces the overhead of the HE primitive compared to the existing implementations.

In this work, we will present a new hybrid cryptography method based on two hard problems: 1- The problem of the discrete logarithm on an elliptic curve defined on a finite local ring. 2- The closest vector problem in lattice and the conjugate problem on square matrices. At first, we will make the exchange of keys to the Diffie-Hellman. The encryption of a message is done with a bad basis of a lattice.

SM2 is an elliptic curve public key cryptography algorithm released by the State Cryptography Administration of China. It includes digital signature, data encryption and key exchange schemes. To meet specific application requirements, such as to protect the user's private key in software only implementation, and to facilitate secure cloud cryptography computing, secret sharing based SM2 signature generation schemes have been proposed in the literature. In this paper a new such kind of scheme based upon additively homomorphic encryption is proposed. The proposed scheme overcomes the drawback that the existing schemes have and is more secure. It is useful in various application scenarios.

This article is an introduction to a well known problem on the ring Fq[e] where e3=e2: Fully homomorphic encryption scheme. In this paper, we introduce a new diagram of encryption based on the conjugate problem on Fq[e] , (ESR(Fq[e])).

This is an extension of an ongoing research project on Fully Homomorphic Encryption. Arithmetic Circuit Homomorphic Encryption (ACHE) [1] was implemented based on (TFHE) Fast Fully Homomorphic Encryption over the Torus. Just like many Homomorphic Encryption methods, ACHE does not integrate with any authentication method. Thus, this was an issue that this paper attempts to resolve. This paper will focus on the implementation method of integrating RFC7664 [2] into ACHE. Next, the paper will further discuss latency incurred due to key generation, the latency of transmission of public and private keys. Last but not least, the paper will also discuss the key size generated and its significance.

Cloud computing is one of the emerging area in the business world that help to access resources at low expense with high privacy. Security is a standout amongst the most imperative difficulties in cloud network for cloud providers and their customers. In order to ensure security in cloud, we proposed a framework using different encryption algorithm namely Extended hill cipher and homomorphic encryption. Firstly user data/information is isolated into two parts which is static and dynamic data (critical data). Extended hill cipher encryption is applied over more important dynamic part where we are encrypting the string using matrix multiplication. While homomorphic encryption is applied over static data in which it accepts n number of strings as information, encode each string independently and lastly combine all the strings. The test results clearly manifests that the proposed model provides better information security.

Cloud computing can be described as a distributed design that is accessible to different forms of security intrusions. An encoding technique named homomorphic encoding is used for the encoding of entities which are utilized for the accession of data from cloud server. The main problems of homomorphic encoding scheme are key organization and key allocation. Because of these issues, effectiveness of homomorphic encryption approach decreases. The encoding procedure requires the generation of input, and for this, an approach named Particle swarm optimization is implemented in the presented research study. PSO algorithms are nature encouraged meta-heuristic algorithms. These algorithms are inhabitant reliant. In these algorithms, societal activities of birds and fishes are utilized as an encouragement for the development of a technical mechanism. Relying on the superiority of computations, the results are modified with the help of algorithms which are taken from arbitrarily allocated pattern of particles. With the movement of particles around the searching area, the spontaneity is performed by utilizing a pattern of arithmetical terminology. For the generation of permanent number key for encoding, optimized PSO approach is utilized. MATLAB program is used for the implementation of PSO relied homomorphic algorithm. The investigating outcomes depicts that this technique proves very beneficial on the requisites of resource exploitation and finishing time. PSO relied homomorphic algorithm is more applicable in terms of completion time and resource utilization in comparison with homomorphic algorithm.

The objective of this research is to develop a novel image encryption method that can be used to considerably increase the security of encrypted images. To solve this image security problem, we propose a distributed homomorphic image encryption scheme where the images of interest are those in the visible electromagnetic spectrum. In our encryption phase, a red green blue (RGB) image is first separated into its constituent channel images, and then the numerical intensity value of a pixel from each channel is written as a sum of smaller pixel intensity sub-values, leading to having several component images for each of the R, G, and B-channel images. A homomorphic encryption function is used to separately encrypted each of the pixel intensity sub-values in each component image using an encryption key, leading to a distributed image encryption approach. Each of the encrypted component images can be compressed before transmission and/or storage. In our decryption phase, each encrypted component image is decompressed if necessary, and then the homomorphic property of the encryption function is used to transform the product of individually encrypted pixel intensity sub-values in each encrypted component images, to the encryption of their sum, before applying the corresponding decryption function with a decryption key to recover the original pixel's intensity values for each channel image, and then recovering the original RGB image. Furthermore, a special case of an RGB image encryption and decryption where a pixel's intensity value from each channel is written as a sum of only two sub-values is implemented and simulated with a software. The resulting cipher-images are subject to a range of security tests and analyses. Results from these tests shown that our proposed homomorphic image encryption scheme is robust and can resist security attacks, as well as increases the security of the associated encrypted images. Our proposed homomorphic image encryption scheme has produced highly secure encrypted images.

With the rapid development of the contemporary society, wide use of smart phone and vehicle sensing devices brings a huge influence on the extensive data collection. Network coding can only provide weak security privacy protection. Aiming at weak secure feature of network coding, this paper proposes an information transfer mechanism, Weak Security Network Coding with Homomorphic Encryption (HE-WSNC), and it is integrated into routing policy. In this mechanism, a movement model is designed, which allows information transmission process under Wi-Fi and Bluetooth environment rather than consuming 4G data flow. Not only does this application reduce the cost, but also improve reliability of data transmission. Moreover, it attracts more users to participate.

Named Data Networking (or NDN) represents a potential new approach to the current host based Internet architecture which prioritize content over the communication between end nodes. NDN relies on caching functionalities and local data storage, such as a content request could be satisfied by any node holding a copy of the content in its storage. Due to the fact that users in the same network domain can share their cached content with each other and in order to reduce the transmission cost for obtaining the desired content, a cooperative network coding mechanism is proposed in this paper. We first formulate our optimal coding and homomorphic signature scheme as a MIP problem and we show how to leverage Software Defined Networking to provide seamless implementation of the proposed solution. Evaluation results demonstrate the efficiency of the proposed coding scheme which achieves better performance than conventional NDN with random coding especially in terms of transmission cost and security.

Generating a secure source of publicly-verifiable randomness could be the single most fundamental technical challenge on a distributed network, especially in the blockchain context. Many current proposals face serious problems of scalability and security issues. We present a protocol which can be implemented on a blockchain that ensures unpredictable, tamper-resistant, scalable and publicly-verifiable outcomes. The main building blocks of our protocol are homomorphic encryption (HE) and verifiable random functions (VRF). The use of homomorphic encryption enables mathematical operations to be performed on encrypted data, to ensure no one knows the outcome prior to being generated. The protocol requires O(n) elliptic curve multiplications and additions as well as O(n) signature signing and verification operations, which permits great scalability. We present a comparison between recent approaches to the generation of random beacons.