Secure Multiparty Research Articles

Privacy-preserving data analysis

With the ever-increasing volume of data being generated and shared across various platforms, the challenge of maintaining privacy while extracting value from this data has become paramount. This paper delves into the realm of Privacy-Preserving Data Analysis (PPDA), examining its current landscape and the pivotal techniques shaping it. Using datasets from diverse domains, we evaluated four leading PPDA techniques—Differential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Data Obfuscation—to discern their efficacy and trade-offs in terms of data utility and privacy breach risk. Our findings underscore the strengths and constraints of each method, guiding researchers and practitioners in choosing the optimal approach for specific scenarios. As data continues to be an invaluable asset in the digital age, the tools and techniques to analyze it privately will play a critical role in shaping future data-driven decision-making processes.

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.

Secure Multiparty Logical AND Based on Quantum Homomorphic Encryption and Its Applications

Secure Multiparty Logical AND Based on Quantum Homomorphic Encryption and Its Applications

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.

Distributed Ledgers and Secure Multiparty Computation for Financial Reporting and Auditing

Distributed Ledgers and Secure Multiparty Computation for Financial Reporting and Auditing

Big Data and Privacy with a focus on Statistical Approaches to Ensuring Data Confidentiality

In the era of Big Data, where vast datasets fuel innovation across industries, the paramount concern remains safeguarding individual privacy. This article explores how statistical approaches ensure data confidentiality amidst the proliferation of digital information. Differential privacy techniques introduce calibrated noise to protect identities while preserving data utility, crucial for compliance and trust in data-driven decision-making. Secure Multiparty Computation (MPC) enables collaborative analysis without exposing raw data, supporting privacy in sectors like healthcare and finance. Privacy-preserving data mining techniques integrate encryption and anonymization to extract insights while shielding sensitive information. Anonymization and de-identification methods further bolster privacy by masking identifiable data, essential for adhering to stringent regulations like GDPR and HIPAA. As data generation escalates, advancing these statistical methods is essential for maintaining privacy integrity in the evolving landscape of Big Data applications. Keywords: Big Data, Privacy, Statistical approaches, Differential privacy, Secure multiparty computation.

SENTINEY: Securing ENcrypted mulTI-party computatIoN for Enhanced data privacY and phishing detection

Phishing attacks have recently become a real danger that threatens the security of sensitive data. This research paper presents a new approach based on Secure Multi-Party Computation (SMPC) to identify phishing attacks on encrypted emails while ensuring data confidentiality and privacy. The proposed approach, SENTINEY (Securing ENcrypted mulTIparty computatIoN for Enhanced data privacY and phishing detection), combines unsupervised machine learning and string matching techniques to detect phishing links over encrypted data, making it adaptive. Subsequently, an adaptive system dynamically selects the appropriate phishing detection technique taking into account various factors (e.g, the volume of new attacks, the accuracy of the machine learning model, attack specificity and available system resources) is suggested. For efficiency reasons, the learning model uses network virtualization features to improve computational resources. This new approach has shown good performance in taking advantage of network virtualization to create a secure and collaborative environment for the SMPC. The dataset used to train and test the proposal is generated on the basis of real phishing emails, including real phishing URLs and keywords. An in-depth performance analysis evaluated the performance of the proposed approach in terms of efficiency (processing time) and robustness (accuracy, precision, recall and F1 score). Simulation results and comparison with relevant solutions show that the proposed approach achieves superior robustness at lower costs. Using a string-matching approach, the multilayer perceptron achieved the highest accuracy of 98% with a detection time of 0.89 s. On the other hand, Isolation Forest showed high efficiency in combating zero-day phishing attacks. The MLP model combined with other tools achieved an accuracy of 99.4%.

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.

Privacy-preserving distributed market mechanism for active distribution networks

Amidst the worldwide efforts to decarbonize power networks, Local Electricity Markets (LEMs) in distribution networks are gaining importance due to the increased adoption of renewable energy sources and prosumers. Considering that LEMs involve data exchange among independent entities, privacy and cybersecurity are some of the main practical challenges in LEM design. This paper proposes a secure market protocol using innovations from distributed optimization and Secure MultiParty Computation (SMPC). The considered LEM is formulated as an uncertainty-aware joint market for energy and reserves with affine balancing policies. To achieve scalability and enable the use of SMPC, market clearing is solved using the Consensus ADMM algorithm. Subsequently, the data exchange among participants via ADMM iterations is protected using the Shamir secret-sharing scheme to ensure privacy. The market protocol is further reinforced by a secure and verifiable settlement process that uses SMPC and ElGamal commitments to verify market quantities and by a secure recovery scheme for missing network measurements. Finally, the feasibility and performance of the proposed LEM are evaluated on a 15-bus test network.

Secure IoT Communication: Implementing a One-Time Pad Protocol with True Random Numbers and Secure Multiparty Sums

We introduce an innovative approach for secure communication in the Internet of Things (IoT) environment using a one-time pad (OTP) protocol. This protocol is augmented by incorporating a secure multiparty sum protocol to produce OTP keys from genuine random numbers obtained from the physical phenomena observed in each device. We have implemented our method using ZeroC-Ice v.3.7, dependable middleware for distributed computing, demonstrating its practicality in various hybrid IoT scenarios, particularly in devices with limited processing capabilities. The security features of our protocol are evaluated under the Dolev–Yao threat model, providing a thorough assessment of its defense against potential cyber threats.

Self-balancing Incremental Broad Learning System with privacy protection

Incremental learning algorithms have been developed as an efficient solution for fast remodeling in Broad Learning Systems (BLS) without a retraining process. Even though the structure and performance of broad learning are gradually showing superiority, private data leakage in broad learning systems is still a problem that needs to be solved. Recently, Multiparty Secure Broad Learning System (MSBLS) is proposed to allow two clients to participate training. However, privacy-preserving broad learning across multiple clients has received limited attention. In this paper, we propose a Self-Balancing Incremental Broad Learning System (SIBLS) with privacy protection by considering the effect of different data sample sizes from clients, which allows multiple clients to be involved in the incremental learning. Specifically, we design a client selection strategy to select two clients in each round by reducing the gap in the number of data samples in the incremental updating process. To ensure the security under the participation of multiple clients, we introduce a mediator in the data encryption and feature mapping process. Three classical datasets are used to validate the effectiveness of our proposed SIBLS, including MNIST, Fashion and NORB datasets. Experimental results show that our proposed SIBLS can have comparable performance with MSBLS while achieving better performance than federated learning in terms of accuracy and running time.

Enhancing Privacy and Security in IoT Environments through Secure Multiparty Computation

Enhancing Privacy and Security in IoT Environments through Secure Multiparty Computation

Privacy-Preserving data analysis

With the ever-increasing volume of data being generated and shared across various platforms, the challenge of maintaining privacy while extracting value from this data has become paramount. This paper delves into the realm of Privacy-Preserving Data Analysis (PPDA), examining its current landscape and the pivotal techniques shaping it. Using datasets from diverse domains, we evaluated four leading PPDA techniquesDifferential Privacy, Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Data Obfuscationto discern their efficacy and trade-offs in terms of data utility and privacy breach risk. Our findings underscore the strengths and constraints of each method, guiding researchers and practitioners in choosing the optimal approach for specific scenarios. As data continues to be an invaluable asset in the digital age, the tools and techniques to analyze it privately will play a critical role in shaping future data-driven decision-making processes.

A federated learning scheme for hierarchical protection and multiple aggregation

Differential privacy, homomorphic encryption, and multi-party security computing can protect exchanged data during federated learning. However, they fail to focus on accuracy, training time, or communication traffic while ensuring security. Therefore, we proposed a federated learning scheme for hierarchical protection and multiple aggregation. Firstly, the client contribution is calculated and compared with the threshold. The local model update is discarded, disturbed, or encrypted according to the comparison result. Secondly, the server adjusts the selection weights of clients and aggregates the disturbed and the encrypted local model updates respectively. Finally, the client gets the final global model by decryption and aggregation. Compared with other schemes, the scheme shows the highest accuracy of 86.15 %, the second lowest communication traffic of 170,261,072 bytes, and the training time is only 3112 s. It not only ensures security but also reduces the training time and communication traffic while improving accuracy.

Secure Multi-Party Linear Algebra with Perfect Correctness

We present new secure multi-party computation protocols for linear algebra over a finite field, which improve the state-of-the-art in terms of security. We look at the case of unconditional security with perfect correctness, i.e., information-theoretic security without errors. We notably propose an expected constant-round protocol for solving systems of m linear equations in n variables over Fq with expected complexity O(k n^2.5 + k m) (where complexity is measured in terms of the number of secure multiplications required) with k > m(m+n)+1. The previous proposals were not error-free: known protocols can indeed fail and thus reveal information with probability Omega(poly(m)/q). Our protocols are simple and rely on existing computer-algebra techniques, notably the Preparata-Sarwate algorithm, a simple but poorly known “baby-step giant-step” method for computing the characteristic polynomial of a matrix, and techniques due to Mulmuley for error-free linear algebra in positive characteristic.

Privacy-Preserving Data Sharing in Healthcare: Advances in Secure Multiparty Computation

Privacy-Preserving Data Sharing in Healthcare: Advances in Secure Multiparty Computation

Blockchain-based collaborative data analysis framework for distributed medical knowledge extraction

To ensure the privacy preservation and transparent use of regulated medical big data at decentralized and distributed medical institutions, this paper proposes a blockchain-based collaborative data analysis framework to realize multiparty secure data sharing and cooperative medical knowledge extraction through a transparent and regulatory machine learning approach. A smart contract is employed on the blockchain as the underlying technique to realize autonomous control and transparent regulation of closed-loop data acquisition and analysis. Considering the execution complexity of smart contracts for analysis collaboration, Petri net is adopted to formulize the workflows of smart contracts, and it acts as the underlying on-chain learning (OcL) approach. Finally, an experimental case study is conducted using real-life medical data to verify and evaluate the effectiveness and efficiency of our framework. A prototype system is established to demonstrate the real-life distributed knowledge extraction demand of our cooperating company. Four groups of experiments are designed and conducted to determine the effectiveness and efficiency of the learning process. The results show that the proposed framework significantly outperforms federated learning (FL) in terms of accuracy on small datasets, where the framework achieves an accuracy of 55.050% compared to FL. Meanwhile, the framework exhibits superior convergence in loss compared to FL, with a difference of 76.663%. In the case of big datasets, the framework achieves a faster completion of model training by 58.883%, with lower CPU utilization by 44.023% and lower memory utilization by 16.227% compared to FL.

Steps Toward a Shared Infrastructure for Multi-Party Secure Private Computing in Official Statistics

Steps Toward a Shared Infrastructure for Multi-Party Secure Private Computing in Official Statistics

Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain.

Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme's efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

A secure multi-party payment channel on-chain and off-chain supervisable scheme

With the increasing demand for digital currencies and other blockchain transactions, the contradiction between the growing demand and the limited blockchain space has become increasingly prominent, and the scalability of the blockchain urgently needs to be resolved. The existing off-chain payment channel solutions do not give enough consideration to the multi-party parallel transaction process, and the transaction process lacks supervision, making it difficult to meet the actual demand. In this paper, we propose a multi-party secure, flexible, concurrent and supervisable off-chain payment channel scheme. Our scheme not only optimizes the current transaction process so that the participants and channel balance within the channel can be flexibly changed without affecting the normal transaction process, but also designs a multi-level collaborative supervision mechanism from multiple perspectives of positive and negative, active and passive, and multiple layers of on-chain and off-chain to ensure the safety and convenience of the transaction process. The security of the scheme was also analyzed, especially the possible collusion in the channel. We simulated the operation of the channel in the blockchain simulation and testing software Simblock and tested the scheme from various aspects. The experiments show that our scheme is able to achieve secure supervision within the channel with a modest overhead (about 15%). Testing of the mentioned smart contracts further illustrates the feasibility of the scheme.