Homomorphic Encryption Method Research Articles

Privacy-preserving and communication-efficient stochastic alternating direction method of multipliers for federated learning

Federated learning constitutes a paradigm in distributed machine learning, wherein model training unfolds through the exchange of intermediary results between a central server and federated clients. Given its decentralized nature, conventional machine learning algorithms find limited applicability in the context of federated learning models. Hence, the alternating direction method of multipliers (ADMM), tailored for distributed optimization, is leveraged for this purpose. However, despite the considerable promise of the ADMM algorithm in federated learning, it faces challenges related to computational efficiency, communication efficiency, and data security. In response to these challenges, this study proposes the privacy-preserving and communication-efficient stochastic ADMM (PPCESADMM) algorithm that enhances the computational efficiency through the stochastic optimization method, reduces communication costs through sparse communication method, and ensures the security of federated clients' data via the homomorphic encryption method. Theoretical analyses confirm the convergence of the PPCESADMM algorithm under mild conditions and establish its convergence rate as O(1/T). Experiments illustrate the superior performance of our algorithm in communication cost compared to ADMM and CEADMM algorithms, achieving reductions of 65.10% and 44.32%, respectively. Furthermore, our method surpasses classical federated learning algorithms such as FedAvg, FedAvgM, and SCAFFOLD in terms of algorithmic convergence, achieving superior convergence precision within predefined training epochs. Finally, our algorithm converges to the same results as those obtained without using homomorphic encryption, albeit at the cost of increased computation time.

Overview of homomorphic encryption technology for data privacy

This study examines the overview of homomorphic encryption technology for data privacy. In the era of big data, the growing need to utilize vast amounts of information while ensuring privacy and security has become a significant challenge. Homomorphic encryption technology has gained attention as a solution for privacy-preserving data processing, allowing computations on encrypted data without exposing sensitive information. This study introduces the concept of data privacy preservation and explores the evaluation of homomorphic encrypted technology. The focus is on analyzing both partial and full homomorphic encryption methods, highlighting their respective characteristics, evaluation criteria, and the current state of research. Partial homomorphic encryption supports limited operations, while full homomorphic encryption enables unlimited computation on encrypted data, though both face challenges related to computational overhead and efficiency. Additionally, this paper addresses the ongoing issues and limitations associated with homomorphic encryption, such as its complexity, large encryption volumes, and difficulties in handling large-scale datasets. Despite these challenges, researchers continue to refine the technology and expand its applications in cloud computing, big data analytics, and privacy-preserving computing environments. This study also discussed potential future research avenues aimed at improving the scalability, efficiency, and security of homomorphic encryption to support broader, real-world applications. Ultimately, homomorphic encryption is positioned as a key enabler for secure data utilization in an increasingly privacy-conscious digital landscape.

Design and Implementation of Energy-Aware Integration Mechanisms for IoT-Cloud Architectures: Enhancing Efficiency in Resource-Constrained Environments

This study paper suggests a way for the Internet of Things (IoT) and Cloud computing to work together that is smart about energy use. The goal is to solve the problem of IoT-Cloud integration's energy efficiency, which is important for making sensor nodes last longer and making sure that data transfer continues over time. The first step in the process is to plan the IoT nodes and give each sensor node a unique ID and a starting energy level. To make data safer, a homomorphic encryption method is used to make public and private keys. The integration process uses a good Cluster Head Selection Technique that has Random, Energy-Based, Distance-Based, and Density-Based methods to make sure that the sensor nodes are grouped together in the best way possible. The sensor nodes send data to the base station through cluster heads, which use homomorphic encryption to make the connection safe. The public key is used to hash each data packet before it is sent to the cluster head. The cluster head then sends the packets to the base station through the Cloud. At the base station, the data hash is checked one at a time, and then the data is decrypted. This makes sure that the data is correct and safe while it is being sent. Comparing time and energy use across different systems and node numbers is part of the performance assessment. The results show that System 4 did better than the others in terms of how much energy it used and how efficiently it transferred data. This shows that the proposed system can effectively handle IoT-Cloud integration. System 4 kept 28,848 units of energy after starting with 29,442 units, showing that it was much more energy efficient. The results show that using techniques that are aware of energy in IoT-Cloud systems can greatly improve performance. This study adds to the growing body of research on integrating IoT and the cloud by showing a safe, effective, and energy-conscious method that sends data while using the least amount of energy possible.

Privacy preserving security using multi‐key homomorphic encryption for face recognition

AbstractRecently, face recognition based on homomorphic encryption for privacy preservation has garnered significant attention. However, there are two major challenges with homomorphic encryption methods: the security and efficiency of face recognition systems. We present a more efficient and secure PUM (Privacy preserving security Using Multi‐key homomorphic encryption) mechanism for facial recognition. By integrating feature grouping with parallel computing, we enhance the efficiency of homomorphic operations. The use of multi‐key encryption ensures the security of the facial recognition system. This approach improves the security and speed of facial recognition systems in cloud computing scenarios, increasing the original 128‐bit security to a maximum of 1664‐bit security. In terms of efficiency, comparing encrypted images takes only 0.302 s, with an accuracy rate of 99.425%. When applied to a campus scenario, the average search time for a facial template library containing 700 encrypted features is approximately 1.5 s. Consequently, our solution not only ensures user privacy but also demonstrates superior operational efficiency and practical value. In comparison to recently emerged ciphertext facial recognition systems, our solution has demonstrated notable enhancements in both security and time efficiency.

Edge server enhanced secure and privacy preserving federated learning

Federated learning (FL) has been smoothly embedded into current IoT edge computing architecture, and hatching advanced IoT-FL applications. While so, sensitive messages generated on terminal sides brings privacy issues; and vulnerable terminal devices are easy targets for Byzantine adversaries, they inject malicious data to manipulate the IoT-FL system. Now an inherent dilemma is, the privacy preserving requires anonymous indistinguishable individual characteristics, while the malicious adversary detection requires transparent distinguishable results. Existing fragmented schemes are not able to handle both problems coherently. This time, inherit our previous study, based on the IoT edge computing architecture, we utilize Homomorphic Encryption (HE) and Threshold Secret Sharing (SS) methods, propose a joint scheme (namely, Sec-IoTFL) for anonymous adversary detection. Specifically, we batch encode the clients’ training result vectors (message) as polynomials, and split them as secret pieces with a unified SS key set; then edge server and cloud server joint deal with these secret pieces in a specialized flow, where Byzantine adversaries will be filtered out. The theoretical analysis and solid experimental results suggest, in our scheme, local sensitive data is well preserved, and malicious behavior is precisely screened. Comparing with traditional methods, our Sec-IoTFL scheme shows the superiority in accuracy and efficiency.

Image ciphertexts classification method based on ghost imaging and intraclass-interclass difference

In this paper, based on ghost imaging encryption, the preservation of Manhattan distance feature in ciphertext compared with plaintext is analyzed by utilizing the intraclass-interclass difference of image classification, and a classification method for image ciphertexts is proposed. After calculating Manhattan distance for both plaintexts and ciphertexts, respectively, the intraclass-interclass difference can be determined. The image that minimizes the intraclass-interclass difference is taken as the centroid to verify the consistency of the classification for various plaintext-ciphertext pairs under the same operation. The feasibility of proposed method is verified by numerical simulations, that the values of ACC and Weighted-F2 can be up to 90% when the MNIST is adopted as the test dataset. The whole process can be regarded as a kind of classification process by homomorphic encryption, however, different from the traditional homomorphic encryption methods based on mathematical model, the proposed method is accomplished based on the optical theory, and it does not require a lot of pre-training through models such as deep learning and neural networks, that means, reducing the computational expenses.

Quantum neural network with privacy protection of input data and training parameters

It takes a lot of computational resources to train a machine learning model. So does quantum machine learning. In the NISQ (Noisy Intermediate Scale Quantum) era, there is a need for users who cannot afford quantum computers to utilize quantum servers to complete quantum machine learning with protection privacy of data and model parameters. In this paper, novel homomorphic encryption methods and circuit design with hidden parameter for Rz, Rx and Ry gate are putting forward. Based on which, we further propose quantum neural network (QNN) with privacy protection of input data and training parameters, where hiding model parameters has hardly mentioned in existing literature. To verify the validity of the proposed scheme, we conducted experiments on MNIST and Iris datasets, and obtained losses and accuracy that are almost identical to the original algorithms. Experiments show that our scheme can protect privacy well in the training and have no effect on accuracy of the algorithm. By comparison, the proposed scheme is more secure than most existing literature and requires less extra complexity O(4n).

Age of Information Optimization for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS)-enabled data collection can be implemented in a cost-effective, scalable, and flexible manner. However, joint sensing data freshness and security assurance have not been fully investigated in the current research. To address these two concerns, the potential game and homomorphic encryption-based joint Age of Information (AoI) optimization and privacy-preservation scheme for MCS is put forward in this paper. At first, the AoI minimization and privacy preservation-oriented MCS system framework is established. Then, the AoI-based spectrum access strategies are derived by a potential game in detail, where the stochastic learning algorithm is used to reach the Nash Equilibrium (NE) solution. Next, based on the somewhat homomorphic encryption method, the encrypted sensing data can be submitted to the service provider (SP) for further processing, where the data content can only be known to mobile workers (MWs) and service requester (SR) with permission. Finally, the numerical results show that our proposed MCS system can simultaneously guarantee data freshness and system security at an acceptable cost.

Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality.

To adhere to and capitalize on the benefits of the FAIR (findable, accessible, interoperable, and reusable) principles in agricultural genome-to-phenome studies, it is crucial to address privacy and intellectual property issues that prevent sharing and reuse of data in research and industry. Direct sharing of genotype and phenotype data is often prohibited due to intellectual property and privacy concerns. Thus, there is a pressing need for encryption methods that obscure confidential aspects of the data, without affecting the outcomes of certain statistical analyses. A homomorphic encryption method for genotypes and phenotypes (HEGP) has been proposed for single-marker regression in genome-wide association studies (GWAS) using linear mixed models with Gaussian errors. This methodology permits frequentist likelihood-based parameter estimation and inference. In this paper, we extend HEGP to broader applications in genome-to-phenome analyses. We show that HEGP is suited to commonly used linear mixed models for genetic analyses of quantitative traits including genomic best linear unbiased prediction (GBLUP) and ridge-regression best linear unbiased prediction (RR-BLUP), as well as Bayesian variable selection methods (e.g. those in Bayesian Alphabet), for genetic parameter estimation, genomic prediction, and GWAS. By advancing the capabilities of HEGP, we offer researchers and industry professionals a secure and efficient approach for collaborative genomic analyses while preserving data confidentiality.

An electronic voting scheme based on homomorphic encryption and decentralization

Compared with paper-based voting, electronic voting not only has advantages in storage and transmission, but also can solve the security problems that exist in traditional voting. However, in practice, most electronic voting faces the risk of voting failure due to malicious voting by voters or ballot tampering by attackers. To solve this problem, this article proposes an electronic voting scheme based on homomorphic encryption and decentralization, which uses the Paillier homomorphic encryption method to ensure that the voting results are not leaked until the election is over. In addition, the scheme applies signatures and two layers of encryption to the ballots. First, the ballot is homomorphically encrypted using the homomorphic public key; then, the voter uses the private key to sign the ballot; and finally, the ballot is encrypted using the public key of the counting center. By signing the ballots and encrypting them in two layers, the security of the ballots in the transmission process and the establishment of the decentralized scheme are guaranteed. The security analysis shows that the proposed scheme can guarantee the completeness, verifiability, anonymity, and uniqueness of the electronic voting scheme. The performance analysis shows that the computational efficiency of the proposed scheme is improved by about 66.7% compared with the Fan et al. scheme (https://doi.org/10.1016/j.future.2019.10.016).

Lattice-Based Homomorphic Encryption For Privacy-Preserving Smart Meter Data Analytics

Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.

TKAGFL: A Federated Communication Framework Under Data Heterogeneity

Federated learning still faces many problems from research to technology implementation and the most critical problem is that the communication efficiency is not high. Therefore, the main task of this work is to improve the communication efficiency, affected by communication devices, bandwidth, data distribution and so on. Our work focus on two main aspects of federated learning — updates strategy and data heterogeneity, and propose a new federated framework called TKAGFL, which is composed by three key factors. Firstly, in response to the problem of data heterogeneity that often occurs in actual federated learning, conditional generative adversarial networks (GANs) are proposed as a method of data preprocessing. Secondly, the improved homomorphic encryption method counterbalances the conflicts between data-sharing and privacy protection. Thirdly, in the communication process, classic AdaGrad optimization in deep learning and top-K algorithms are combined to compress communication parameters to improve communication efficiency. The results of experiments demonstrate our TKAGFL framework's accuracy is about 15% ~ 20% better than other algorithms or frameworks and it can converge at 150 communication rounds and is 50 rounds faster than others. Also, our TKAGFL algorithm reduces 10 times communication volume than other algorithms,which is beneficial for federated learning application in industry.

An Enhanced Blockchain Based Security and Attack Detection Using Transformer In IOT-Cloud Network

The IoT (Internet of Things) encompasses numerous networks and connected devices. One of the primary concerns surrounding IoT, according to researchers and security experts, is the potential risks to privacy and cybersecurity. Deep learning offers significant capabilities for self-adjustment, self-organization, and generalization. Recognizing this, advanced deep learning algorithms are employed in this research to address the privacy and security issues plaguing the IoT landscape. To address these concerns, a novel model called BC-Trans Network is proposed, leveraging the strengths of both Blockchain technology and a transformer component. The transformer plays a vital role in identifying abnormal data, enabling the system to take proactive measures against potential threats. In addition Hash-2 is introduced for the verification of IoT users, adding an extra layer of security to the authentication process. The Blockchain model is utilized to securely store user passwords and details, ensuring a robust and tamper-proof authentication mechanism. To validate the proposed model, a publicly available dataset CSE-CIC-IDS2018 is employed. Pre-processing techniques, including feature selection using the chi-square method, are applied to refine the dataset. The transformer module then classifies the data as normal or abnormal, allowing for accurate identification of potential security breaches. To further safeguard the data and protect the privacy of users, a Fully Homomorphic Encryption (FHE) method is employed. This advanced encryption enables the encryption of categorized normal data, ensuring its confidentiality even during transmission and storage. The study's findings support IoT-cloud server security and privacy by demonstrating the effectiveness of the suggested paradigm in identifying and thwarting network threats. With detection times of 225.3 seconds, an accuracy of 99.25%, a precision of 99.53%, a recall of 99.32%, and an F1 score of 99.59%, the proposed system exhibits impressive performance. Furthermore, as the output numbers increase, the system's metrics improve, suggesting its scalability and flexibility.

An efficient and secure key management with the extended convolutional neural network for intrusion detection in cloud storage

SummaryCloud computing aids users for storing and recovering their information everywhere in the world. Security and efficiency are the two main issues in cloud service. Numerous intrusion detection techniques for the cloud computing environment were proposed, but those techniques do not effectively and accurately detect the attacks. Hence, an efficient and secure key management using extended convolutional neural network, that is, hybrid Enhanced Elman Spike convolutional Neural Network optimized with improved COOT optimization algorithm (Hyb EESCCNN) is proposed for intrusion detection in cloud system. Furthermore, the novel Adaptive Tangent Brakerski‐Gentry Vaikuntanathan Homomorphic Encryption (ATBGVHE) method is proposed for providing the security of the system. At first, SHA‐512 is used for authenticating cloud users to store its own information in to the cloud server. Then for Intrusion Detection (ID) the input data from the NSL‐KDD, UNSWNB15, CICIDS2018, and ToN‐IoT datasets are pre‐processed. The most relevant features are extracted using Fast Independent Component Analysis (Fast ICA) from the pre‐processed output. These extracted data are classified into malicious and non‐malicious data using Hyb EESCCNN. After classification, the non‐malicious data is secured using an ATBGVHE technique. The outcomes of the proposed methods shows that the NSL‐KDD datasets attains 99.9% higher accuracy, UNSW‐NB15 datasets offer 99.89% higher accuracy, CSE‐CIC‐IDS2018 datasets attains 99.8% higher accuracy, ToN‐IoT datasets attains 99.8% higher accuracy and 0.02 s lower encryption time compared with existing methods. Finally, case study with real time applications is also analyzed to prove the efficiency of the proposed method.

Deep learning for content-based image retrieval in FHE algorithms

AbstractContent-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.

English

Huge diagrams have unique properties for organizations and research, such as client linkages in informal organizations and customer evaluation lattices in social channels. They necessitate a lot of financial assets to maintain because they are large and frequently continue to expand. Owners of large diagrams may need to use cloud resources due to the extensive arrangement of open cloud resources to increase capacity and computation flexibility. However, the cloud's accountability and protection of schematics have become a significant issue. In this study, we consider calculations for security savings for essential graph examination practices: schematic extraterrestrial examination for outsourcing graphs in the cloud server. We create the security-protecting variants of the two proposed Eigen decay computations. They are using two cryptographic algorithms: additional substance homomorphic encryption (ASHE) strategies and some degree homomorphic encryption (SDHE) methods. Inadequate networks also feature a distinctively confidential info adaptation convention to allow the trade-off between secrecy and data sparseness. Both dense and sparse structures are investigated. According to test results, calculations with sparse encoding can drastically reduce information. SDHE-based strategies have reduced computing time, while ASHE-based methods have reduced stockpiling expenses.

An Upgraded Data Security Based on Homomorphic Encryption and Aggregate Signature Method in Wireless Sensor Network

Wireless sensor networks (WSN) have been implemented in nearly every field of use because they offer a solution to practical problems that can also be affordably implemented. The sensor nodes have limited computing resources, weak batteries, and limited storage space. The environmental or physical data collected by these nodes is transmitted straight to the BS. The data transfer cost is raised due to the direct data transmission. In addition, the lifetime of sensor networks is shortened because of the rise in energy required for data exchange. As a result, data aggregation is utilized in WSN to lessen the burden of transmission costs and lengthen the useful life of the sensor networks. Each sensor node's transmission is encrypted with cipher text generated by the Paillier homomorphic cryptosystem. In addition, the Bilinear aggregate signature method is used to create a digital signature at each sensor node. The cluster head BS is where the aggregation takes place once the cipher text and signature have been combined. Before deciding whether to accept or reject the message, the BS checks the aggregate signature. The homomorphic cryptosystem saves power because it does not perform intermediate-level or cluster-head decryption. Data integrity, authenticity, and confidentiality are all maintained while using less power with this technology. The Intel laboratory dataset is used in the implementation. When compared to current systems, the proposed SDA method requires less time and energy to calculate.

Secure Formation Control via Edge Computing Enabled by Fully Homomorphic Encryption and Mixed Uniform-Logarithmic Quantization

Recent developments in communication technologies, such as 5G, together with innovative computing paradigms, such as edge computing, provide further possibilities for the implementation of real-time networked control systems. However, privacy and cyber-security concerns arise when sharing private data between sensors, agents and a third-party computing facility. In this letter, a secure version of the distributed formation control is presented, analyzed and simulated, where gradient-based formation control law is implemented in the edge, with sensor and actuator information being secured by fully homomorphic encryption method based on learning with error (FHE-LWE) combined with a proposed mixed uniform-logarithmic quantizer (MULQ). The novel quantizer is shown to be suitable for realizing secure control systems with FHE-LWE where the critical real-time information can be quantized into a prescribed bounded space of plaintext while satisfying a sector bound condition whose lower and upper-bound can be made sufficiently close to an identity. An absolute stability analysis is presented, that shows the asymptotic stability of the closed-loop secure control system.

PMHE: a wearable medical sensor assisted framework for health care based on blockchain and privacy computing

Nowadays, smart medical cloud platforms have become a new direction in the industry. However, because the medical system involves personal physiological data, user privacy in data transmission and processing is also easy to leak in the smart medical cloud platform. This paper proposed a medical data privacy preserving framework named PMHE based on blockchain and fully homomorphic encryption technology. The framework receives personal physiological data from wearable devices on the client side, and uses blockchain as data storage to ensure that the data cannot be tampered with or forged; Besides, it uses fully homomorphic encryption method to design disease prediction models implemented by smart contracts. In PMHE, data is encoded and encrypted on the client side, and encrypted data is uploaded to the cloud platform via the public Internet, preventing privacy leakage caused by channel eavesdropping; smart contracts run on the blockchain platform for disease prediction, and the operators participating in computing are encrypted user data too. So, privacy and security issues caused by platform data leakage are avoided. The client-to-cloud interaction protocol is also designed to overcome the defect that fully homomorphic encryption only supports addition and multiplication by submitting tuples on the client side, to ensure that the prediction model can perform complex computing. In addition, the design of the smart contract is introduced in detail, and the performance of the system is analyzed. Finally, experiments are conducted to verify the operating effect of the system, ensuring that user privacy is not leaked without affecting the accuracy of the model, and realizing a smart medical cloud platform in which data can be used but cannot be borrowed.

High payload watermarking based on enhanced image saliency detection

Nowadays, images are circulated rapidly over the internet and they are subject to some risk of misuses. To address this issue, various watermarking methods are proposed in the literature. However, most conventional methods achieve a certain trade-off among imperceptibility and high capacity payload, and they are not able to improve these criteria simultaneously. Therefore, in this paper, a robust saliency-based image watermarking method is proposed to achieve high payload and high quality watermarked image. First, an enhanced salient object model is proposed to produce a saliency map, followed by a binary mask to segments the foreground/background region of a host image. The same mask is then consulted to decompose the watermark image. Next, the RGB channels of the watermark are encrypted by using Arnold, 3-DES and multi-flipping permutation encoding (MFPE). Furthermore, the principal key used for encryption is embedded in the singular matrix of the blue channel. Moreover, the blue channel is encrypted by using the Okamoto-Uchiyama homomorphic encryption (OUHE) method. Finally, these encrypted watermark channels are diffused and embedded into the host channels. When the need arises, more watermarks can be embedded into the host at the expense of the quality of the embedded watermarks. Our method can embed watermark of the same dimension as the host image, which is the first of its kind. Experimental results suggest that the proposed method maintains robustness while achieving high image quality and high payload. It also outperforms the state-of-the-art (SOTA) methods.