Secure Multi-party Computing Research Articles

Privacy-Preserving Electric Vehicle Charging Recommendation by Incorporating Full Homomorphic Encryption and Secure Multi-Party Computing

Electric vehicle (EV) charging recommendation can significantly improve global planning performance, corresponding to an increasing risk of privacy leakage. Based on this, this paper investigates the privacy data preservation strategy during the interaction between EVs and charging facilities. It proposes a privacy preservation strategy that aims to ensure EV information security. In a cloud computing environment, users do not want other users and cloud providers to have access to their personal information, which is precisely the problem that secure multi-party computing (SMPC) can solve. At present, full homomorphic encryption (FHE) can solve the problem of user data privacy preservation in cloud computing and big data environments and can realize the whole encryption process. Therefore, a more reasonable charging station selection scheme is provided under the computation of privacy preservation strategies incorporating the FHE-SMPC method. The effectiveness and implementation feasibility of the designed privacy preservation strategy in practical applications is verified through testing and comparative analysis. The results show that the developed strategy can significantly reduce the risk of privacy leakage with limited communication resources and computation time consumption. The results provide new perspectives and methodologies for interactive privacy preservation between EVs and charging stations, with application potential.

P-Chain: Towards privacy-aware smart contract using SMPC

Smart contract, as the representative application of blockchain, has recently fueled extensive research interests from both academia and industry. However, with its wide applications, the weaknesses of smart contract have been gradually revealed. The major barrier to the widespread adoption of smart contract involves concerns about on-chain privacy which refers to the details of input/output privacy. To address privacy concerns, we propose in this paper, P-Chain, a privacy-aware framework for smart contracts of permissioned blockchain to protect sensitive data of users based on Secure Multi-party Computation (SMPC). Unlike existing work that suffer several key drawbacks, including introducing a third party who could get the details of the deal, and high overhead for on-chain and off-chain communication, as well as lacking a privacy protection for output data, we enhance the privacy protection for smart contracts system by adding a new secure multi-party computation layer in P-Chain. Through secure multi-party computing, sensitive inputs of smart contracts are divided into multiple sub-inputs and sent to computing participants for operation respectively, which ensures that each participant can only access part of the user’s information. A stochastic strategy based on (t;n) threshold secret sharing to select calculating parties is also been proposed, which makes it difficult for an attacker to aggregate t of n participants for launching a collusive attack. In addition, we propose the output privacy protection method that makes it possible to reach a consensus without the need to know the output. The extensive experimental evaluation and analysis demonstrate that our scheme enjoys the advantages of calculation correctness, input–output privacy as well as anti-collusion.

Advanced Encryption Techniques in Healthcare IoT: Securing Patient Data in Connected Medical Devices

As a result of the fast development of Internet of Things (IoT) technology, the healthcare sector has undergone a transformation. This transformation has been brought about by the deployment of linked medical devices that provide immediate monitoring and data collecting. Despite the fact that these innovations promise to bring about considerable gains in patient care and operational efficiency, they also bring about major security issues, especially with regard to the protection of personally identifiable information about patients. Within the context of the Internet of Things (IoT) ecosystem for healthcare, this study investigates sophisticated encryption approaches that are aimed to protect patient data stored in linked medical equipment. One of the first things that the research does is investigate the specific security needs and risks that are linked with Internet of Things (IoT) devices in the healthcare industry. These include concerns around data privacy, integrity, and authentication. Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Quantum Key Distribution (QKD) are some of the encryption standards and protocols that are often used in the process of protecting Internet of Things (IoT) connections. This article presents a complete examination of these encryption standards and protocols. Following that, it digs into sophisticated encryption approaches that are especially targeted to the limits and needs of healthcare IoT systems. These techniques include lightweight cryptographic algorithms, key management strategies, and secure multi-party computing (MPC) frameworks.

Blockchain-assisted Verifiable Secure Multi-Party Data Computing

Secure multi-party computation (SMPC) is a crucial technology that supports privacy preservation, enabling multiple users to perform computations on any function without disclosing their private inputs and outputs in a distrustful environment. Existing secure multi-party computation models typically rely on obfuscation circuits and cryptographic protocols to facilitate collaborative computation of tasks. However, the efficiency and privacy leakage of users have not been paid much attention during the computation process. To address these problems, this article proposes a privacy-preserving approach Blockchain-assisted Verifiable Secure Multi-Party Data Computing (BVS-MPDC). Specifically, to prevent privacy leakage when users and multiple participants share data, BVS-MPDC uses additive homomorphic encryption to encrypt data shares; and verifies the generated Pedersen commitment of all the data. BVS-MPDC utilizes an improved Schnorr aggregation signature to improve computation efficiency between computing nodes and smart contracts by submitting an aggregation signature to the blockchain. Moreover, we design and implement a smart contract for verifying aggregation signature results on Ethereum. The security proof is presented under the UC framework. Finally, simulation experiments of performance evaluations demonstrate that our scheme outperforms existing schemes in computation overhead and verification.

Quantum secure multiparty computing XOR protocol based on single photons and its application in quantum secure communications for intelligence agents

In this paper, we consider an interesting and important privacy-preserving issue, i.e., how to implement anonymous and secure communications for several intelligence agents, hiding in n participants. To solve this issue, we first propose a quantum Secure Multiparty Computing XOR (SMC_XOR) protocol based on single photons, which can guarantee the unconditional security of the protocol. By implementing rotation encryption, the practicality of quantum SMC_XOR protocol can be significantly improved without other complex quantum techniques. Security analysis shows that the proposed protocol can resist various types of attacks. Furthermore, a special network model is designed to solve this issue, using hash function to verify the identity of the communication parties and key recycling to reduce resource consumption. Finally, the proposed quantum SMC_XOR protocol is simulated in IBM Qiskit, and the simulation results show that the protocol is correct and feasible.

Measurement‐Device‐Independent Quantum Secret Sharing

AbstractQuantum secret sharing has many important applications in quantum communication and secure multiparty computing. In this work, a novel measurement‐device‐independent protocol for three‐party quantum secret sharing is put forward, in which the dealer and two sharers are required to prepare Greenberger‐Horne‐Zeilinger states and single particles, respectively, but they only need to perform single‐particle measurement. As a result, this protocol is feasible in experiments by using current technologies. More importantly, it can extend the spatial distance among three participants for quantum state transmissions at least 15.47% compared to the previous protocols.

LF3PFL: A Practical Privacy-Preserving Federated Learning Algorithm Based on Local Federalization Scheme.

In the realm of federated learning (FL), the exchange of model data may inadvertently expose sensitive information of participants, leading to significant privacy concerns. Existing FL privacy-preserving techniques, such as differential privacy (DP) and secure multi-party computing (SMC), though offering viable solutions, face practical challenges including reduced performance and complex implementations. To overcome these hurdles, we propose a novel and pragmatic approach to privacy preservation in FL by employing localized federated updates (LF3PFL) aimed at enhancing the protection of participant data. Furthermore, this research refines the approach by incorporating cross-entropy optimization, carefully fine-tuning measurement, and improving information loss during the model training phase to enhance both model efficacy and data confidentiality. Our approach is theoretically supported and empirically validated through extensive simulations on three public datasets: CIFAR-10, Shakespeare, and MNIST. We evaluate its effectiveness by comparing training accuracy and privacy protection against state-of-the-art techniques. Our experiments, which involve five distinct local models (Simple-CNN, ModerateCNN, Lenet, VGG9, and Resnet18), provide a comprehensive assessment across a variety of scenarios. The results clearly demonstrate that LF3PFL not only maintains competitive training accuracies but also significantly improves privacy preservation, surpassing existing methods in practical applications. This balance between privacy and performance underscores the potential of localized federated updates as a key component in future FL privacy strategies, offering a scalable and effective solution to one of the most pressing challenges in FL.

Practical solutions in fully homomorphic encryption: a survey analyzing existing acceleration methods

Fully homomorphic encryption (FHE) has experienced significant development and continuous breakthroughs in theory, enabling its widespread application in various fields, like outsourcing computation and secure multi-party computing, in order to preserve privacy. Nonetheless, the application of FHE is constrained by its substantial computing overhead and storage cost. Researchers have proposed practical acceleration solutions to address these issues. This paper aims to provide a comprehensive survey for systematically comparing and analyzing the strengths and weaknesses of FHE acceleration schemes, which is currently lacking in the literature. The relevant researches conducted between 2019 and 2022 are investigated. We first provide a comprehensive summary of the latest research findings on accelerating FHE, aiming to offer valuable insights for researchers interested in FHE acceleration. Secondly, we classify existing acceleration schemes from algorithmic and hardware perspectives. We also propose evaluation metrics and conduct a detailed comparison of various methods. Finally, our study presents the future research directions of FHE acceleration, and also offers both guidance and support for practical application and theoretical research in this field.

Multiparty Secure Broad Learning System for Privacy Preserving.

Multiparty learning is an indispensable technique to improve the learning performance via integrating data from multiple parties. Unfortunately, directly integrating multiparty data could not meet the privacy-preserving requirements, which then induces the development of privacy-preserving machine learning (PPML), a key research task in multiparty learning. Despite this, the existing PPML methods generally cannot simultaneously meet multiple requirements, such as security, accuracy, efficiency, and application scope. To deal with the aforementioned problems, in this article, we present a new PPML method based on the secure multiparty interactive protocol, namely, the multiparty secure broad learning system (MSBLS) and derive its security analysis. To be specific, the proposed method employs the interactive protocol and random mapping to generate the mapped features of data, and then uses efficient broad learning to train the neural network classifier. To the best of our knowledge, this is the first attempt for privacy computing method that jointly combines secure multiparty computing and neural network. Theoretically, this method can ensure that the accuracy of the model will not be reduced due to encryption, and the calculation speed is very fast. Three classical datasets are adopted to verify our conclusion.

Quick Text Classification with Privacy Protection based on Secure Multiparty Computing

Abstract: I intend and complete activity a insured Naive Bayes sign to resolve the issue of private theme request. Alice, on one side, is holding a text, and Impact, on the other, is holding a classifier. Bob won't have advanced by any means, and Alice will just know how the calculation answered her text at the show's decision. Reaction depends on Secure Multiparty Computation (SMC). Utilizing Rust variant, it is basic and get to classify unstructured text. I can determine if a SMS is marketing mail or hot dog in inferior 340 milliseconds when Impact's model's declaration remark breadth covers all dispute (n = 5200) and Alice's SMS has m = 160 unigrams. This plan is adjustable and maybe secondhand in miscellaneous position place the Earnest Bayes classifier maybe secondhand. For n = 369 and m = 8, the sane number of marketing mail SMSs in the educational group, plan just takes 21 milliseconds

Robust peer-to-peer learning via secure multi-party computation

To solve the data island problem, federated learning (FL) provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation. Peer-to-peer (P2P) federated learning further improves the robustness of the system, in which there is no server and each client communicates directly with the other. For secure aggregation, secure multi-party computing (SMPC) protocols have been utilized in peer-to-peer manner. However, the ideal SMPC protocols could fail when some clients drop out. In this paper, we propose a robust peer-to-peer learning (RP2PL) algorithm via SMPC to resist clients dropping out. We improve the segment-based SMPC protocol by adding a check and designing the generation method of random segments. In RP2PL, each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training. Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.

A Semi-Quantum Private Comparison Base on W-States.

Privacy comparison is an important research topic in secure multi-party computing, widely used in e-commerce, secret ballots, and other fields. However, the development of quantum computing power poses a growing potential security threat to secure multi-party algorithms based on mathematically tricky problems, and most of the proposed quantum privacy comparison schemes could be more efficient. Therefore, based on the W-state, we offer a more efficient semi-quantum privacy comparison method. The security analysis shows that the scheme can resist third-party, measurement, and entanglement attacks. Compared with the previous work, the scheme significantly improves communication efficiency and has stronger practicability.

Distributed Energy Storage Data Sharing Based on Privacy Protection using Secure Multiparty Computation

Background:: Data sharing can improve the utilization rate of distributed energy storage and solve the problem of data silos, but there are privacy and data security issues in distributed energy storage data sharing. Objective:: To address the privacy and security issues in distributed energy storage data sharing. Method:: In this paper, a distributed privacy-preserving data sharing scheme based on secure multi-party computing is proposed. Firstly, the requirements for distributed energy storage data sharing are analyzed, and the concept of distributed energy storage data sharing is defined. Secondly, the additive secret sharing technology is adopted to realize the safe transmission and storage of energy storage data between energy storage nodes. Finally, combined with the relevant forgetting transmission extension protocol, privacy-preserving data computing is realized, so as to solve the problem of secure sharing of distributed energy storage data. Results:: The simulation results show that the privacy protection method does not affect the convergence round of specific communication. However, in order to achieve the effect of privacy protection, the number of communication times has become twice that of conventional communication, and the privacy protection communication time accounts for about 0.8 of the total communication time. Conclusion:: Compared with the traditional data-sharing method, the proposed method realizes privacy-preserving data sharing without affecting algorithm convergence. For distributed scenarios, a flexible P2P sharing architecture is proposed, which has strong scalability and regret, and has good application prospects. conclusion: Experimental results show that compared with traditional data sharing methods, the proposed method has lower computational cost, achieves privacy-protecting data sharing, has strong scalability and deplorability, and good application prospects. other: No attention

Literature Review on Improving IoT Security Using Machine and Deep Learning Technique

This literature review paper discusses the concept of the Internet of Things (IoT) and its implications on various domains, highlighting the challenges and security concerns associated with its expansive scope. This review work emphasizes the need for comprehensive security solutions to address the complexities of IoT infrastructure, particularly in the context of emerging threats. Thispaper also underscores the importance of integrating security, energy efficiency, software applications, and data analytics in IoT systems. It outlines the evolving landscape of IoT security, including the vulnerabilities and potential consequences of inadequate security measures. Additionally, authors address the intersection of security and privacy concerns within Deep Learning (DL) and Machine Learning (ML), discussing various strategies such as homomorphic encryption, differential privacy, trusted execution, and secure multiparty computing. It acknowledges the computational demands of these approaches and the ongoing search for globally harmonized solutions. Finally, authors concludes by highlighting the challenges and strategies in countering adversarial attacks in DL and ML, emphasizing the effectiveness of adversarial training and the multifaceted nature of defense mechanisms.

A privacy-preserving trading strategy for blockchain-based P2P electricity transactions

With the rising demand for energy in distribution networks and the growing development of communication technology, peer-to-peer (P2P) electricity trading realized by blockchain technology not only promotes the participation of distributed generation resources in the energy supply but also enhances the fairness of P2P electricity trading through the decentralized and transparent characteristics of the blockchain. However, owing to the openness and transparency of the on-chain data in the blockchain, the order information submitted by P2P market participants to the blockchain may encounter threats of information disclosure. To address this challenge, we have designed a novel three-layer architecture for P2P electricity trading, whose primary objective is to protect the private information submitted to the blockchain through a new privacy-preserving trading strategy. The proposed three-layer architecture is composed of a physical layer, information layer and market layer. An on-chain data protection method with homomorphic encryption and secure multi-party computing (SMPC) is used to sort and clear encrypted orders in the information layer, and the trading checking and safe settlement process of encrypted orders in continuous double auction (CDA) market are realized through grid security-constrained market mechanism in the market layer. In addition, the effectiveness of the proposed method is verified by case studies running on the IBM Hyperledger Fabric.

Secure Multi-party θ-join Algorithms Toward Data Federation

PDF HTML XML Export Cite reminder Secure Multi-party θ-join Algorithms Toward Data Federation DOI: 10.21655/ijsi.1673-7288.00298 Author: Affiliation: Clc Number: Fund Project: Article | Figures | Metrics | Reference | Related | Cited by | Materials | Comments Abstract:Recently, many countries and regions have enacted data security policies, such as the General Data Protection Regulation proposed by the EU. The release of related laws and regulations has aggravated the problem of data silos, which makes it difficult to share data among various data owners. Data federation is a possible solution to this problem. Data federation refers to the calculation of query tasks jointly performed by multiple data owners without original data leaks using privacy computing technologies such as secure multi-party computing. This concept has become a research trend in recent years, and a series of representative systems have been proposed, such as SMCQL and Conclave. However, for the core join queries in the relational database system, the existing data federation system still has the following problems. First of all, the join query type is single, which is difficult to meet the query requirements under complex join conditions. Secondly, the algorithm performance has huge improvement space because the existing systems often call the security tool library directly, which means the runtime and communication overhead is high. Therefore, this paper proposes a join algorithm under data federation to address the above issues. The main contributions of this paper are as follows: firstly, multi-party-oriented federation security operators are designed and implemented, which can support many operations. Secondly, a federated θ-join algorithm and an optimization strategy are proposed to significantly reduce the security computation cost. Finally, the performance of the algorithm proposed in this paper is verified by the benchmark dataset TPC-H. The experimental results show that the proposed algorithm can reduce the runtime and communication overhead by 61.33% and 95.26%, respectively, compared with the existing data federation systems SMCQL and Conclave. Reference Related Cited by

A Blockchain-Based Secure Multi-Party Computation Scheme with Multi-Key Fully Homomorphic Proxy Re-Encryption

At present, secure multi-party computing is an effective solution for organizations and institutions that want to derive greater value and benefit from the collaborative computing of their data. Most current secure multi-party computing solutions use encryption schemes that are not resistant to quantum attacks, which is a security risk in today’s quickly growing quantum computing, and, when obtaining results, the result querier needs to collect the private keys of multiple data owners to jointly decrypt them, or there needs to be an interaction between the data owner and the querier during the decryption process. Based on the NTRU cryptosystem, which is resistant to quantum computing attacks and has a simple and easy-to-implement structure, and combined with multi-key fully homomorphic encryption (MKFHE) and proxy re-encryption, this paper proposes a secure multi-party computing scheme based on NTRU-type multi-key fully homomorphic proxy re-encryption in the blockchain environment, using the blockchain as trusted storage and a trusted execution environment to provide data security for multi-party computing. The scheme meets the requirements of being verifiable, conspiracy-proof, individually decryptable by the querier, and resistant to quantum attacks.

Privacy-Preserving Convolution Neural Network Inference with Edge-assistance

Nowadays the wide application of Convolution Neural Network(CNN) has promoted the rapid development of new intelligence fields. However, the inference of complex CNN usually includes a large number of computational operations. It is hard for the resource-limited user to complete the complicated CNN inference by his own. Privacy-preserving convolution neural network inference schemes allow the user to leverage the edge/cloud server to complete CNN inference under the condition that the privacy is protected. Nevertheless, existing schemes either require time-consuming cryptographic techniques, such as secure multi-party computing and homomorphic encryption, or fail to protect the privacy of the trained CNN model. To address this problem, we propose a novel privacy-preserving CNN inference scheme with edge-assistance. In this scheme, the privacy of the input data and the trained model is well protected and no complex cryptographic technique is involved. We blind the data to be predicted and the trained model to the edge servers, and then the edge servers calculate the most time-consuming layers. The user only deals with computationally efficient layers, fast encryption and recovery. Analysis and experimental results demonstrate that the proposed scheme is secure and efficient, which obviously saves the computational overhead of the user.

FROM THE PERSPECTIVE OF EMOTIONAL LABOR AND WORK PRESSURE, THIS PAPER DISCUSSES HOW JOINT ORDERING STRATEGY CAN IMPROVE THE PSYCHOLOGICAL SAFETY OF SUPPLY CHAIN MEMBERS

BackgroundIt is of great economic significance to optimize the total cost of supply chain and improve the performance of supply chain. Information sharing can improve supply chain performance. A probabilistic demand system with one seller and one buyer studied by scholars can find that compared with the traditional decentralized system, the quantity coordination strategy can improve the performance of the supply chain. However, supply chain members are reluctant to provide private cost information because their customers may abuse this information, which will seriously affect their psychological security of privacy protection. The application of secure multiparty computing (SMC) is a possible way to solve these problems. SMC provides a framework for computing partners to make decisions to achieve global goals while protecting the privacy of any party's private information. Only a few literatures have discussed the application of SMC in supply chain collaboration. When applying SMC protocol to the joint ordering strategy with quantity discount under Probabilistic demand, the inventory problem has not been studied. Especially in terms of the actual indicators of emotional labor and work stress, no research has been carried out.Subjects and MethodsFirstly, we analyze the optimal order quantity under Probabilistic demand, the optimal joint order quantity under Probabilistic demand, profit distribution and quantity discount design. Then, based on the SMC protocol, a joint ordering strategy under Probabilistic demand is proposed, and the corresponding privacy protection quantity discount of the joint ordering strategy without intermediary is given. In this regard, we give the numerical simulation of joint ordering and the numerical simulation of privacy protection joint ordering strategy and its quantity discount design. Last, Using the revised Lin Shangping's “organization's emotional labor burden scale” and “Minnesota Satisfaction Scale (MSQ), the study distributed 200 questionnaires to the subjects and recovered 196. The sample structure distribution is that men account for 51.7%, the lowest proportion under the age of 25 (2.5%), and the highest proportion over the age of 36 (62.6%) The proportion is the highest, accounting for 64.0%. In terms of service years, the proportion of 1-3 years is the lowest, accounting for 4.4%, and the proportion of more than 7 years is the highest, accounting for 84.2%. The total correlation coefficient and total reliability of the modified items were analyzed to delete the items without internal consistency in the questionnaire, so as to improve the reliability of the questionnaire and the quality of measurement tools.ResultsThe privacy protection joint ordering strategy based on the probabilistic demand of SMC protocol can be implemented under the condition of any private (cost and capacity) information retention, which can be realized without mediation. The numerical simulation results of joint ordering show that joint ordering can reduce the total cost of supply chain. Through quantity discount design, the cost of each participant in the supply chain can be reduced to varying degrees. In terms of emotional labor, it has a positive and significant impact on job satisfaction, because appropriate emotional labor helps to reduce work pressure and naturally improve psychological safety.ConclusionSMC protocol gives the privacy protection joint ordering strategy under Probabilistic demand, and determines the privacy protection quantity discount of the joint ordering strategy under Probabilistic demand. The numerical simulation results of joint ordering show that joint ordering can reduce the total cost of supply chain. Through quantity discount design, the cost of each participant in the supply chain can be reduced to varying degrees. The above privacy protection agreement can be realized without the help of intermediaries. The buyer and the seller can obtain the optimal joint order quantity while protecting their own privacy information, which helps to improve the psychological security of supply chain members, and realize efficient operation and win-win of the supply chain. In the future, we will consider the existence of more buyers or multi-layer supply chain structure, and deduce the corresponding privacy protection algorithm.AcknowledgementsSupported by a project grant from Zhejiang Philosophy and Social Sciences Foundation (Grant No.19NDJC145YB), and a project grant from National Natural Science Foundation of China (Grant No. 71203161), and a project grant from National Social Science Foundation of China (Grant No. 17BGL132).

Research on the Millionaires’ Problem under the Malicious Model Based on the Elliptic Curve Cryptography

With the rapid development of blockchain, big data, cloud computing, and artificial intelligence, the security of multisource data collaborative computing has become increasingly prominent. Secure multiparty computing has become the core technology of privacy collaborative computing. Millionaires’ problem is the cornerstone of secure multiparty computation. Firstly, this paper proposes a 0-1 coding rule, which is used to solve the millionaires’ problem under the semihonest model. Aiming at the possible malicious behaviors of the protocol under the semihonest model, the millionaires’ problem protocol under the malicious model based on the elliptic curve cryptography is designed by using cryptographic tools such as the zero-knowledge proof and the cut-choose method. This protocol not only can effectively solve the millionaires’ problem but also can safely and effectively prevent malicious behaviors. Meanwhile, the security ordering designed by the protocol can be effectively applied to a quality evaluation in the blockchain.