Paillier Homomorphic Encryption Research Articles

Privacy-preserving Scanpath Comparison for Pervasive Eye Tracking

As eye tracking becomes pervasive with screen-based devices and head-mounted displays, privacy concerns regarding eye-tracking data have escalated. While state-of-the-art approaches for privacy-preserving eye tracking mostly involve differential privacy and empirical data manipulations, previous research has not focused on methods for scanpaths. We introduce a novel privacy-preserving scanpath comparison protocol designed for the widely used Needleman-Wunsch algorithm, a generalized version of the edit distance algorithm. Particularly, by incorporating the Paillier homomorphic encryption scheme, our protocol ensures that no private information is revealed. Furthermore, we introduce a random processing strategy and a multi-layered masking method to obfuscate the values while preserving the original order of encrypted editing operation costs. This minimizes communication overhead, requiring a single communication round for each iteration of the Needleman-Wunsch process. We demonstrate the efficiency and applicability of our protocol on three publicly available datasets with comprehensive computational performance analyses and make our source code publicly accessible.

EPFL-DAC: Enhancing Privacy in Federated Learning with Dynamic Aggregation and Clipping

Federated learning achieves data privacy protection by uploading only users’ local updates. However, recent research has shown that privacy data can still be inferred from local updates. Privacy-preserving federated learning also faces challenges related to the confidentiality and privacy of local updates, the robustness of dynamic membership schemes, and the determination of appropriate clipping threshold in differentially private federated learning. To address these issues, this paper presents the EPFL-DAC scheme: Enhancing Privacy in Federated Learning with Dynamic Aggregation and Clipping. Firstly, Paillier homomorphic encryption is employed to securely aggregate local updates, ensuring that the server can only access the aggregated information. Secondly, a dynamic robustness algorithm is designed to ensure the robustness of the aggregation scheme when user states change. Lastly, a dynamic threshold determination method is introduced to introduce more accurate noise, while also providing resistance against collusive attacks in differentially private settings. Experimental results demonstrate the effectiveness, low computational overhead, and preservation of accuracy in the dynamic aggregation scheme. Additionally, the proposed dynamic threshold determination method exhibits good performance, especially at higher privacy protection levels and larger initial clipping threshold values.

Design of Privacy Protection System for Target Search Based on Selection Algorithm and Sustainable Iteration Index

The data self-destruct method based on privacy cloud proposed in this paper can realize the automatic destruction of cloud data after expiration, and based on this method, a self-destruct image protection system is implemented. This project intends to combine the adaptive scale invariant Feature conversion (SIFT) extraction of ciphertext images with binary SIFT to ensure the confidentiality of the images uploaded to the server in the ciphertext space. The security protection of data is realized using Paillier homomorphic encryption technology. The feature of SIFT is extracted from the encrypted region, and it is expressed in binary form to reduce the amount of computation and memory overhead. The experimental results show that this method can effectively prevent the automatic destruction of images after expiration, so as to ensure that the user's personal information is not leaked.

Enhanced Secure Storage of Big Data at Rest with Improved ECC and Paillier Homomorphic Encryption Algorithms

Enhanced Secure Storage of Big Data at Rest with Improved ECC and Paillier Homomorphic Encryption Algorithms

Smart Grids Data Aggregation Method on Paillier Homomorphic Encryption

ABSTRACT Smart grid (SGs) is a highly integrated power system, it is gradually replacing the traditional power grid, but at present there are problems in the SGs data aggregation, such as user privacy leakage, grid data query inflexibly and data leakage. In order to solve these problems, this paper studied the SGs data aggregation method based on Paillier homomorphic encryption (HE). In this paper, the Paillier HE algorithm was used to study the power grid data, and the encrypted data was numerically calculated under different encryption states. It used the cloud computing center in the blockchain to directly aggregate the ciphertext data generated by users, and used the hash function to set the secret key to prevent the ciphertext data from being tampered with, and obtained the aggregate result of the original data after decryption by the power grid management center. When the number of security parameters was 30,000, the encryption time required by this method was 21.71 seconds. When the number of smart meters (SMs) was 80, the signature verification time required by this method was 2.31 seconds.

Homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography

AbstractExisting copyright protection schemes for 3D models of oblique photography predominantly utilize digital watermarking technology, most of which suffer from shortcomings such as the potential false accusation of legitimate users and the inability to effectively trace unauthorized distributors. In response to these challenges, this article proposes a homomorphic encryption domain asymmetric fingerprinting scheme for 3D models of oblique photography. Initially, the fingerprints to be embedded are collaboratively generated by the transacting parties, with the publisher remaining unaware of the user's actual fingerprint. This approach ensures the effective implementation of the asymmetric nature of the fingerprinting scheme. Subsequently, potential geometric and non‐geometric attacks are considered prior to fingerprint embedding. Robustness is enhanced for the proposed solution through relative coordinate transformations and normalization procedures. Ultimately, the embedding and extraction of fingerprints are achieved through the application of an effective bit parity check method, in conjunction with the vertex‐paradigm projection length mapping mechanism and the Paillier homomorphic encryption system, minimizing embedding distortion while ensuring data security. Experimental results demonstrate that the proposed solution exhibits satisfying robustness against common manipulations, such as cropping, simplification, and geometric attacks. Additionally, the scheme effectively safeguards legitimate users from false accusations and accurately traces maliciously authorized users.

EPFed: Achieving Optimal Balance between Privacy and Efficiency in Federated Learning

Federated learning (FL) is increasingly challenged by security and privacy concerns, particularly vulnerabilities exposed by malicious participants. There remains a gap in effectively countering threats such as model inversion and poisoning attacks in existing research. To address these challenges, this paper proposes the Effective Private-Protected Federated Learning Aggregation Algorithm (EPFed), a framework that utilizes a blockchain platform, homomorphic encryption, and secret sharing to fortify the data privacy and computational efficiency in a federated learning environment. EPFed works by establishing “trust groups” through the unique integration of a Chinese Remainder Theorem-based secret sharing scheme with Paillier homomorphic encryption, streamlining secure model parameter exchange and aggregation while minimizing the computational load. Our performance-driven aggregation strategy leverages local performance metrics to safeguard against malicious contributions, ensuring both the integrity and efficiency of the learning process. The evaluations demonstrate that EPFed achieves a remarkable accuracy rate of 92.5%, thereby confirming the advanced nature of the proposed solution in addressing the pressing challenges of FL.

Cryptanalysis of a privacy-preserving authentication scheme based on private set intersection

Abstract Continuous and context-aware authentication mechanisms have been proposed as complementary security mechanisms to password-based authentication for computer devices that are handled directly by humans, such as smart phones. Such authentication mechanisms incur some privacy issues as user-dependent features are revealed to the authentication server, which is assumed to be untrusted. Domingo-Ferrer et al. proposed a privacy-preserving protocol for context-aware user authentication on the basis of private set intersection and Paillier homomorphic encryption. This approach enables user authentication based on establishing the number of similarities between sampled user context data and reference context data, without revealing any plaintext data to either party. The authors claim that their scheme is secure against malicious adversaries. In this article, we show that Domingo-Ferrer et al.’s scheme is insecure by means of two undetectable attacks that reveal all user information despite the encryption. The Paillier encryption primitive has a homomorphic property that we observe not only lacks relevance but, indeed, incurs a vulnerability that is exploited in the proposed cryptanalysis. This means that special care needs to be taken considering homomorphic properties of cryptographic primitives used in cryptographic protocols. Our cryptanalysis may therefore have a general interest regarding the design of cryptographic protocols.

Multidimensional Epidemiological Survey Data Aggregation Scheme Based on Personalized Local Differential Privacy

In recent years, with the rapid development of intelligent technology, information security and privacy issues have become increasingly prominent. Epidemiological survey data (ESD) research plays a vital role in understanding the laws and trends of disease transmission. However, epidemiological investigations (EI) involve a large amount of privacy-sensitive data which, once leaked, will cause serious harm to individuals and society. Collecting EI data is also a huge task. To solve these problems and meet personalized privacy protection requirements in EIs, we improve the uOUE protocol based on utility-optimized local differential privacy to improve the efficiency and accuracy of data coding. At the same time, aiming at the collection and processing of ESD, a multidimensional epidemiological survey data aggregation scheme based on uOUE is designed. By using Paillier homomorphic encryption and an identity-based signature scheme to further prevent differential attacks and achieve multidimensional data aggregation, the safe, efficient, and accurate aggregation processing of ESD is executed. Through security proof and performance comparison, it is verified that our algorithm meets the requirements of local differential privacy and unbiased estimation. The experimental evaluation results on two data sets show that the algorithm has good practicability and accuracy in ESD collection and provides reliable and effective privacy protection.

Secure multi-party computation in deep learning : Enhancing privacy in distributed neural networks

Ensuring data privacy while applying Deep Learning (DL) on distributed datasets represents an essential task in the current period of critical data security. Data privacy and accuracy of models are typically impacted by traditional methods. Data privacy is of the most tremendous significance in distributed data settings, and the current research presents a novel model for DL termed Secure Multi-Party Computation (SMPC). The accuracy of the mathematical models and confidentiality of the data are frequently compelled to coexist in conventional methods. In order to enable collaborative DL without compromising private information, the recommended system uses the Paillier Homomorphic Encryption Scheme (PHES). By using innovative cryptographic methods, this decentralized method secures the confidentiality of data without utilizing a Trusted Authority (TA). By performing a thorough assessment of the CIFAR-10 and IMDB datasets, the present study demonstrates that the system the author uses is simple and scalable and that it offers accuracy on par with conventional methods. By presenting an approach for achieving a balance between the two competing demands of data security and computational performance, this method signifies a vast advance forward with a confidentiality DL.

Enhancing Security and Privacy in IoT-Based Learning with Homomorphic Encryption

The security and privacy of data in an IoT-driven intelligence landscape is a major concern. This research examines the integration of Paillier homomorphic encryption into Federated Learning to enhance security while maintaining individual data privacy in such environments. The interconnectedness of devices in IoT frameworks poses a challenge in maintaining the confidentiality of sensitive information. By using Paillier encryption within Federated Learning, this problem is solved by securing learning parameters while still keeping data private. This approach demonstrates promising improvements without violating privacy through extensive simulations and comparative analyses across different model architectures. The results of this study highlight the potential effectiveness of this method for enhancing security measures in interconnected IoT environments.

Development of computer application system and database testing based on data encryption technology

Abstract In this paper, we use the MapReduce programming framework for the development of a computer application system and design the vision module, control module and transaction module of the system based on MVC architecture. Focusing on the encryption design of the database, the data stored in the database is chunked through the Map function, and all the chunking results are aggregated based on the Reduce function, and synchronized encryption of the database is combined with the Paillier homomorphic encryption algorithm. On this basis, the system environment is configured, and the application system developed in this paper is tested, focusing on exploring the stability of the database. The test results show that the decryption output time is 21s when the Paillier encryption algorithm is 200, and the first set of database peak is 76% of the server load, which is within the tolerable range. Security test on the database: the results show that the probability of an attack on all ports with a database security factor less than 6 is around 0.3. There is no significant difference, and the database test is good.

A Secure Multi-Party Computation Protocol for Graph Editing Distance against Malicious Attacks

The secure computation of the graph structure is an important element in the field of secure calculation of graphs, which is important in querying data in graphs, since there are no algorithms for the graph edit distance problem that can resist attacks by malicious adversaries. In this paper, for the problem of secure computation of similarity edit distance of graphs, firstly, the encoding method applicable to the Paillier encryption algorithm is proposed, and the XOR operation scheme is proposed according to the Paillier homomorphic encryption algorithm. Then, the security algorithm under the semi-honest model is designed, which adopts the new encoding method and the XOR operation scheme. Finally, for the malicious behaviors that may be implemented by malicious participants in the semi-honest algorithm, using the hash function, a algorithm for secure computation of graph editing distance under the malicious model is designed, and the security of the algorithm is proved, and the computational complexity and the communication complexity of the algorithm are analyzed, which is more efficient compared with the existing schemes, and has practical value. The algorithm designed in this paper fills the research gap in the existing literature on the problem of graph edit distance and contributes to solving the problem.

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).

A Privacy-Preserving Data Aggregation Scheme Based on Chinese Remainder Theorem in Mobile Crowdsensing System

Mobile crowdsensing provides a large-scale reliable data, since sensing nodes support wide distribution, flexible mobility, and opportunistic connectivity. The generated data often contains the sensing node's sensitive information. Existing schemes dedicated to protecting the privacy of perception data, but these schemes cannot balance the computational and communication overheads well. Therefore, we propose a privacy-preserving data aggregation scheme based on the Chinese remainder theorem. Using blinding factor and Paillier homomorphic encryption technology, which not only ensures the privacy of perceived data but also improves the robustness of the system. Specially, the introduction of secure multicast communication technology based on the Chinese remainder theorem protects task privacy and ensures only designated sensing nodes can obtain the task. In addition, we design an efficient signature scheme to ensure data integrity. Detailed security analysis shows that our scheme is existentially unforgeable against adaptively chosen message attack. Extensive experiments and performance analysis show that our scheme is efficient and has a low communication overhead.

BeDCV: Blockchain-Enabled Decentralized Consistency Verification for Cross-Chain Calculation

With the increase of data stored on the blockchain, the efficiency of storage and calculation of blockchain has gradually become a bottleneck restricting the development of blockchain. By storing data on multiple chains, blockchains can request data from other chains for calculation and the storage pressure can be alleviated. But the transfer of a large amount of data between chains suffers from low transfer efficiency and poor security. A reasonable design is to perform the calculation on the data storage chain and only transfer the results across chains. However, since the calculation process is invisible, blockchains cannot judge the consistency of calculation results from other chains. In this paper, we provide a blockchain-enabled decentralized consistency verification scheme for cross-chain calculation (BeDCV). Considering the decentralized characteristic of blockchain, we adopt the blockchain called <i>supervision chain</i> for decentralized auditing. We modify paillier homomorphic encryption to encrypt data involved in the calculation for correctness verification. Then, we aggregate the ciphertexts of data to generate the audit proof for integrity verification. Besides, we verify whether the data involved in the calculation are real-time by leveraging a counting bloom filter. The supervision chain can check the correctness, integrity, and real-time performance of cross-chain data calculation without revealing any original information about the data. The theoretical and experimental analysis demonstrates that BeDCV can verify the consistency of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.

A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

This paper presents a hybrid blockchain-edge architecture for managing Electronic Health Records (EHRs) with attribute-based cryptographic mechanisms. The architecture introduces a novel attribute-based signature aggregation (ABSA) scheme and multi-authority attribute-based encryption (MA-ABE) integrated with Paillier homomorphic encryption (HE) to protect patients&#x2019; anonymity and safeguard their EHRs. All the EHR activities and access control events are recorded permanently as blockchain transactions. We develop the ABSA module on Hyperledger Ursa cryptography library, MA-ABE module on OpenABE toolset, and blockchain network on Hyperledger Fabric. We measure the execution time of ABSA&#x2019;s signing and verification functions, MA-ABE with different access policies and homomorphic encryption schemes, and compare the results with other existing blockchain-based EHR systems. We validate the access activities and authentication events recorded in blockchain transactions and evaluate the transaction throughput and latency using Hyperledger Caliper. The results show that the performance meets real-world scenarios&#x2019; requirements while safeguarding EHR and is robust against unauthorized retrievals.

PDPChain: A Consortium Blockchain-Based Privacy Protection Scheme for Personal Data

With the advances and innovations in digital technologies, blockchain has empowered advancements in communications and networking, promising to build trust and establish secure decentralized communications networks. Unfortunately, current personal data privacy protection schemes still suffer from explicit storage, lack of data ownership and implementation of fine-grained access control by users, and lack of transparency and auditability of data. In this article, we propose a personal data privacy protection scheme based on consortium blockchain that stores original data encrypted with an improved Paillier homomorphic encryption mechanism, namely PDPChain, where users realize fine-grained access control based on ciphertext policy attribute-based encryption (CP-ABE) on blockchain. In this scheme, consortium blockchain combines distributed private clusters to store the encrypted data, improving data transmission efficiency, and guaranteeing user privacy and security through off-chain storage and on-chain transmission synergy. In addition, it is more lightweight encryption and demarcation, ultimately protecting personal data privacy and providing a secure and trusted way to obtain information for data mining. For the performance testing, data in the form of files are used as an example, and the scheme is designed and simulated on Hyperledger Fabric and InterPlanetary File System. Experimental results show that the improved Paillier encryption mechanism reduces the overall encryption and decryption elapsed time by 25&#x0025; and encryption elapsed time by 48&#x0025;. Furthermore, the proposed CP-ABE access control method is adaptive to storing and sharing a massive amount of data. With the increase in the number of access control policies, the overall time-consuming of the scheme does not increase, and the time-consuming of decryption can also be stabilized at about 2&#x00A0;s.

PPVerifier: A Privacy-Preserving and Verifiable Federated Learning Method in Cloud-Edge Collaborative Computing Environment

Federated Learning is a distributed machine learning framework, which mainly adopts cloud-edge collaborative computing mode and supports multiple participants to train models without directly sharing local data. However, participants’ sensitive information may still be leaked through their gradients. Besides, incorrect aggregated results returned by the aggregation server may reduce the effect of joint modeling. This paper proposes a privacy-preserving and verifiable federated learning method called PPVerifier to support privacy protection and verification of aggregated results in cloud-edge collaborative computing environment. By integrating Paillier homomorphic encryption and random number generation technique, all gradients and their ciphertexts can be protected. Meanwhile, an additive secret sharing scheme is introduced to resist potential collusion attacks among the aggregation server, malicious participants, and edge nodes. Moreover, a verification scheme based on discrete logarithm is proposed, which can not only verify the correctness of aggregated results, but also discover lazy aggregation servers, and the verification overhead can be reduced by over half compared with bilinear aggregate signature method. Finally, theoretical analysis and experiments conducted on MNIST dataset prove that our proposed method can achieve gradient protection and correctness verification of the aggregated results with higher efficiency.

A certificateless multi-dimensional data aggregation scheme for smart grid

In the context of the smart grid, the use of smart meters to regularly collect electricity data from customers poses a significant risk of privacy disclosure. Furthermore, traditional data aggregation schemes assume a fully trusted authority, neglecting the possibility of malicious trusted third parties and collusive attacks from control centers and gateways, which raises privacy invasion concerns for users. To address this issue, this article proposes a multi-dimensional data aggregation scheme based on certificateless public key cryptography for smart grid, which utilizes Paillier homomorphic encryption system in a fog computing-based architecture and implements authentication and key negotiation among member users. This scheme is designed to resist collusive attacks from control centers (CC) and fog nodes (FNs), while also protecting users’ privacy even in the face of a malicious key generation center (KGC). Detailed security analysis demonstrates that the proposed scheme meets the security requirements of the smart grid. Additionally, Performance analysis shows that the proposed scheme can effectively reduce the computational overhead of both smart meters and aggregators compared to existing multidimensional data aggregation schemes.