Secure Multiparty Computation Protocols Research Articles

PrivData Network: A Privacy-Preserving On-Chain Data Factory and Trading Market

Privacy concerns often raise when sensitive data are collected, traded, and processed. The data owner typically loses her ultimate control of the data after data-outsourcing. In this work, we present the PrivData Network – a community-controlled privacy-preserving data factory and trading market. It can be viewed as a standalone data ecosystem that enables privacy-preserving data-driven workflows in a controlled environment for orchestrating and automating data movement and data transformation. In particular, we design a data encapsulation mechanism with privacy assurance, which can guarantee data privacy, usage policy compliance and metadata validity. We also design a privacy policy language and utilize a static analysis library that transfers the program to the defined policy language. To ensure the correctness of data processing, we propose a publicly verifiable secure multiparty computation protocol for mixed circuits, which guarantees the output correctness even if all parties are corrupted. Its online efficiency is comparable to conventional semi-honest secret-sharing-based MPC schemes. Finally, we implemented a prototype of our system in C++ and benchmark it on various tasks, such as biometric matching, logistic regression, and decision trees, etc.

Confidentially Compare Rational Numbers under the Malicious Model

<p>Secure multi-party computation is a hotspot in the cryptography field, and it is also a significant means to realize privacy computation. The Millionaires’ problem is the most fundamental problem among them, which is the basic module of secure multi-party computation protocols. Although there are many solutions to this problem, there are few anti-malicious adversarial protocols besides protocols based on Yao’s garbled circuit. Only a few solutions have low efficiency, and there is no protocol for rational numbers comparison under the malicious model, which restricts the solution of many secure multi-party computation problems. In this paper, the possible malicious behaviors are analyzed in the existing Millionaires’ problem protocols. These behaviors are discovered and taken precautions against through the triangle area formula, zero-knowledge proof, and cut-and-choose method, so the protocol of comparing confidentially rational numbers is proposed under the malicious model. And this paper adopts the real/ideal model paradigm to prove the security of the malicious model protocol. Efficiency analysis indicates that the proposed protocol is more effective than existing protocols. The protocol of rational numbers comparison under the malicious model is more suitable for the practical applications of secure multi-party computation, which has important theoretical and practical significance.</p> <p> </p>

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.

Extending the Security of SPDZ with Fairness

SPDZ refers to a family of protocols for Secure Multi-Party Computation (MPC) that lie at the foundation of very popular software frameworks for MPC, such as SCALE-MAMBA and MP-SPDZ. SPDZ provides good efficiency while guaranteeing security even when all but one of the participants are corrupted. This seemingly optimal property comes at a price: the protocol only offers security with abort, meaning that even a single cheating participant can force the protocol to abort, leaving honest participants with no clue on what the correct output is, or who cheated. This is especially problematic since cheating participants are able to obtain the correct output of the computation, effectively `stealing' it. We propose a *hybrid secure* adaptation to SPDZ, which retains the existing security guarantees, but in case the number of cheating players is less than half of the total, we achieve *fairness*, meaning that either all players obtain the correct output of the computation, or no player does. The `less than half' threshold of corrupted players has been proven to be a tight bound to achieve fairness. Aside from the description of the protocol and its security proof, we also present a proof-of-concept implementation, and evaluate its practical performance, thereby demonstrating that our solution has negligible overhead compared to standard SPDZ in most application scenarios.

Quantum secure multi-party computational geometry based on multi-party summation and multiplication

Secure multi-party computational geometry is a branch of secure multi-party computation, which is applied in many important fields. But up to now, the research on how to solve this problem with quantum methods has just started. Therefore, we study the design of quantum secure multi-party computational geometry (QSMCG) protocols in this paper. As the foundation, we extend the two-party summation and multiplication protocols proposed by Sutradhar and Om to multi-party protocols. Then we propose three new QSMCG protocols. Our proposed quantum secure two-party distance computation protocol has improvement in efficiency and accuracy. And the proposed quantum secure multi-party polytope volume computation protocol as well as the quantum secure circle–circle intersection area computation protocol are designed for the first time. Moreover, these protocols are designed to use multi-third parties, which solves the problem of intermediate value leakage in computation. The correctness, security and performance of all these protocols are analyzed.

Local Community Detection in Multiple Private Networks

Individuals are often involved in multiple online social networks. Considering that owners of these networks are unwilling to share their networks, some global algorithms combine information from multiple networks to detect all communities in multiple networks without sharing their edges. When data owners are only interested in the community containing a given node, it is unnecessary and computationally expensive for multiple networks to interact with each other to mine all communities. Moreover, data owners who are specifically looking for a community typically prefer to provide less data than the global algorithms require. Therefore, we propose the Local Collaborative Community Detection problem (LCCD). It exploits information from multiple networks to jointly detect the local community containing a given node without directly sharing edges between networks. To address the LCCD problem, we present a method developed from M method, called colM, to detect the local community in multiple networks. This method adopts secure multiparty computation protocols to protect each network’s private information. Our experiments were conducted on real-world and synthetic datasets. Experimental results show that colM method could effectively identify community structures and outperform comparison algorithms.

Prioritization and exchange chains in privacy-preserving kidney exchange

The Kidney Exchange Problem (KEP) aims at finding an optimal set of exchanges among pairs of patients and their medically incompatible living kidney donors as well as altruistic donors who are not associated with any particular patient but want to donate a kidney to any person in need. Existing platforms that offer the finding of such exchanges for patient-donor pairs and altruistic donors are organized in a centralized fashion and operated by a single platform operator. This makes them susceptible to manipulation and corruption. Recent research has targeted these security issues by proposing decentralized Secure Multi-Party Computation (SMPC) protocols for solving the KEP. However, these protocols fail to meet two important requirements for kidney exchange in practice. First, they do not allow for altruistic donors. While such donors are not legally allowed in all countries, they have been shown to have a positive effect on the number of transplants that can be found. Second, the existing SMPC protocols do not support prioritization, which is used in existing platforms to give priority to certain exchanges or patient-donor pairs, e.g., to patients who are hard to match due to their medical characteristics. In this paper, we introduce a generic gate for implementing prioritization in kidney exchange. We extend two existing SMPC protocols for solving the KEP such that they allow for altruistic donors and prioritization and present one novel SMPC protocol for solving the KEP with altruistic donors and prioritization based on dynamic programming. We prove the security of all protocols and analyze their complexity. We implement all protocols and evaluate their performance for the setting where altruistic donors are legally allowed and for the setting where they are not. Thereby, we determine the performance impact of the inclusion of altruistic donors and obtain those approaches that perform best for each setting.

Computation and communication efficient approach for federated learning based urban sensing applications against inference attacks

Federated learning based participatory sensing has gained much attention lately for the vital task of urban sensing due to privacy and security issues in conventional machine learning. However, inference attacks by the honest-but-curious application server or a malicious adversary can leak the personal attributes of the participants, such as their home and workplace locations, routines, and habits. Approaches proposed in the literature to prevent such information leakage, such as secure multi-party computation and homomorphic encryption, are infeasible for urban sensing applications owing to high communication and computation costs due to multiple rounds of communication between the user and the server. Moreover, for effective modeling of urban sensing phenomenon, the application model needs to be updated frequently — every few minutes or hours, resulting in periodic data-intensive updates by the participants, which severely strains the already limited resources of their mobile devices. This paper proposes a novel low-cost privacy-preserving framework for enhanced protection against the inference of participants’ personal and private attributes from the data leaked through inference attacks. We propose a novel approach of strategically leaking selected location traces by providing computation and communication-light direct (local) model updates, whereas the rest of the model updates (when the user is at sensitive locations) are provided using secure multi-party computation. We propose two new methods based on spatiotemporal entropy and Kullback–Leibler divergence for automatically deciding which model updates need to be sent through secure multi-party computation and which can be sent directly. The proposed approach significantly reduces the computation and communication overhead for participants compared to the fully secure multi-party computation protocols. It provides enhanced protection against the deduction of personal attributes from inferred location traces compared to the direct model updates by confusing the application server or malicious adversary while inferring personal attributes from location traces. Numerical experiments on the popular Geolife GPS trajectories dataset validate our proposed approach by reducing the computation and communication requirements by the participants significantly and, at the same time, enhancing privacy by decreasing the number of inferred sensitive and private locations of participants.

General quantum secure multiparty computation protocol for simultaneous summation and multiplication

Abstract Quantum secure multiparty computation occupies an important place in quantum cryptography. Based on access structure and linear secret sharing, we propose a new general quantum secure multiparty computation protocol for simultaneous summation and multiplication in a high-dimensional quantum system. In our protocol, each participant within any authorized sets only needs to perform local Pauli operation once on the generalized Bell state, then the summation and multiplication results can be output simultaneously, which improves the practicality of the protocol. Moreover, in the privacy computation phase, the decoy particle detection technique as well as the addition of random numbers are applied to blind the privacy information, making our protocol higher privacy protection. Security analysis shows that our protocol is resistant to a series of typical external attacks and dishonest internal participant attacks such as individual attack and collusion attack. Finally, compared with the existing protocols, our protocol not only has higher efficiency but also lower consumption.

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.

Genomic Data Analysis with Variant of Secure Multi-Party Computation Technique

The increasing availability of genomic data for research purposes necessitates innovative approaches to ensure privacy while facilitating collaborative analysis. This study explores the integration of a variant of Secure Multi-Party Computation (SMPC) techniques into genomic data analysis. The conventional challenges of sharing sensitive genetic information among multiple entities, such as research institutions or healthcare providers, are addressed by leveraging advanced cryptographic protocols. The research focuses on the development and implementation of a secure framework for collaborative genomic data analysis using an adapted SMPC variant. This variant is designed to efficiently handle the complexities of genetic data while ensuring robust privacy preservation. By encrypting individual genomic inputs and enabling computations without revealing the raw data, the proposed SMPC variant facilitates joint analyses, contributing to advancements in personalized medicine, disease research, and genetic epidemiology. The variants of SMPC, namely oblivious transfer protocol, is used, this allows the receiver to obtain one out of several pieces of information forwarded by the sender without revealing which one they obtained. It can be integrated into SMPC protocols for enhancing the privacy with less effort and cost. The proposed mechanism involves the validation of the SMPC variant through simulations using real-world genomic datasets and assessing its performance in terms of computational efficiency and privacy preservation. Results from experiments demonstrate the feasibility and effectiveness of the proposed technique in enabling secure multi-party genomic data analysis. This research contributes to the evolving landscape of privacy-preserving techniques in genomics, offering a promising avenue for collaborative research without compromising the confidentiality of sensitive genetic information.

More efficient constant-round secure multi-party computation based on optimized Tiny-OT and half-gate

Constand-round secure multi-party computation enables a group of mutually distrustful parties to jointly compute a function of their inputs in a constant-round of communication. In recent years, the constant-round secure multi-party computation protocol has improved the efficiency of the online phase by completing the multiplication computation in advance of the online phase through the Beaver triples generated in the preprocessing phase. However, the preprocessing phase of these protocols is extremely overhead-intensive and there is room for improvement. In this paper, we propose an active, constant-round secure multi-party computation (MPC) protocol which can tolerate arbitrary n − 1 corruptions. At the high level, we follow the Tiny-OT protocol and the unauthenticated garbling for garbled circuits in the preprocessing phase presented by Carmit Hazay and Peter Scholl (ASIACRYPT 2017). Particularly, we first present a new optimized Tiny-OT protocol for generating Beaver (AND) triples which is more efficient than the previous protocol, and then combine the information generated by Tiny-OT to construct an unauthenticated garbled circuit. To be precise, we also reduce the number of commitments and hash function calls in the correctness check of AND triples, and avoid using additional OT operations to compute the circuit, thus further improving communication efficiency. Our new constant-round MPC protocol has a communication complexity O(|C|Bnk) per party. Moreover, we further provide solutions to the open problem of introducing the half-gate technique into multi-party distributed garbled, which will reduce half size of the garbled table per gate.

Federated Secure Computing

Privacy-preserving computation (PPC) enables encrypted computation of private data. While advantageous in theory, the complex technology has steep barriers to entry in practice. Here, we derive design goals and principles for a middleware that encapsulates the demanding cryptography server side and provides a simple-to-use interface to client-side application developers. The resulting architecture, “Federated Secure Computing”, offloads computing-intensive tasks to the server and separates concerns of cryptography and business logic. It provides microservices through an Open API 3.0 definition and hosts multiple protocols through self-discovered plugins. It requires only minimal DevSecOps capabilities and is straightforward and secure. Finally, it is small enough to work in the internet of things (IoT) and in propaedeutic settings on consumer hardware. We provide benchmarks for calculations with a secure multiparty computation (SMPC) protocol, both for vertically and horizontally partitioned data. Runtimes are in the range of seconds on both dedicated workstations and IoT devices such as Raspberry Pi or smartphones. A reference implementation is available as free and open source software under the MIT license.

Redactable consortium blockchain based on verifiable distributed chameleon hash functions

With the evolving application demands, the inherent immutability of consortium blockchains hinders their widespread adoption. For example, expired data stored on the chain cannot be deleted, and erroneous data cannot be redacted, seriously limiting the flexibility of consortium blockchains. However, existing redactable blockchain solutions need to be improved in aspects of decentralization, efficiency, and fault tolerance. This paper develops a new verifiable distributed chameleon hash (VDCH) function to solve the above problems. With VDCH, nodes share chameleon keys with a secure multi-party computation protocol based on a verifiable key-sharing scheme, and the collision shares can be verified with a Schnorr non-interactive zero-knowledge proof protocol, which enhances the fault tolerance of the consortium chain while maintaining its decentralized nature. Then, this paper proposes a consensus protocol called CVTSS based on verifiable threshold signatures, which provides protocol support for collaborative hash collision computation by multiple nodes using VDCH, thus avoiding the dependence on Nakamoto consensus and improving the redaction efficiency. Meanwhile, CVTSS uses threshold signatures to prevent malicious nodes from tampering with data using one-time chameleon keys. Finally, this paper constructs an efficient, practical, and secure redactable consortium chain scheme based on VDCH and CVTSS. Theoretical analysis and experimental results show that the proposed scheme can operate safely in the presence of malicious nodes with an acceptable time cost.

Preserving Privacy for Distributed Genome-Wide Analysis Against Identity Tracing Attacks

Genome-wide analysis has demonstrated both health and social benefits. However, large scale sharing of such data may reveal sensitive information about individuals. One of the emerging challenges is identity tracing attack that exploits correlations among genomic data to reveal the identity of DNA samples. In this paper, we first demonstrate that the adversary can narrow down the sample&#x2019;s identity by detecting his/her genetic relatives and quantify such privacy threat by employing a Shannon entropy-based measurement. For example, we exemplify that when the dataset size reaches 30&#x0025; of the population, for any target from that population, the uncertainty of the target&#x2019;s identity is reduced to merely 2.3 bits of entropy (i.e., the identity is pinned down within 5 people). Direct application of existing approaches such as differential privacy (DP), secure multiparty computation (MPC) and homomorphic encryption (HE) may not be applicable to this challenge in genome-wide analysis because of the compromise on utility (i.e., accuracy or efficiency). Towards addressing this challenge, this paper proposes a framework named <small><inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula>Frag</small> to facilitate privacy-preserving data sharing and computation in genome-wide analysis. <small><inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula>Frag</small> mitigates privacy risks by using a vertical fragmentation to disrupt the genetic architecture on which the adversary relies for identity tracing without sacrificing the capability of genome-wide analysis. We theoretically prove that it preserves the correctness of the primitive functionalities and algorithms ranging from basic summary statistics to advanced neural networks. Our experiments demonstrate that <small><inline-formula><tex-math notation="LaTeX">$\upsilon$</tex-math></inline-formula>Frag</small> outperforms secure multiparty computation (MPC) and homomorphic encryption (HE) protocols, with a speedup of more than 221x for training neural networks, and also traditional non-private algorithms and a state-of-the-art noise-based differential privacy (DP) solution in most settings.

Convolutions in Overdrive: Maliciously Secure Convolutions for MPC

Machine learning (ML) has seen a strong rise in popularity in recent years and has become an essential tool for research and industrial applications. Given the large amount of high quality data needed and the often sensitive nature of ML data, privacy-preserving collaborative ML is of increasing importance. In this paper, we introduce new actively secure multiparty computation (MPC) protocols which are specially optimized for privacy-preserving machine learning applications. We concentrate on the optimization of (tensor) convolutions which belong to the most commonly used components in ML architectures, especially in convolutional neural networks but also in recurrent neural networks or transformers, and therefore have a major impact on the overall performance. Our approach is based on a generalized form of structured randomness that speeds up convolutions in a fast online phase. The structured randomness is generated with homomorphic encryption using adapted and newly constructed packing methods for convolutions, which might be of independent interest. Overall our protocols extend the state-of-the-art Overdrive family of protocols (Keller et al., EUROCRYPT 2018). We implemented our protocols on-top of MP-SPDZ (Keller, CCS 2020) resulting in a full-featured implementation with support for faster convolutions. Our evaluation shows that our protocols outperform state-of-the-art actively secure MPC protocols on ML tasks like evaluating ResNet50 by a factor of 3 or more. Benchmarks for depthwise convolutions show order-of-magnitude speed-ups compared to existing approaches.

SMPC-Ranking: A Privacy-Preserving Method on Identifying Influential Nodes in Multiple Private Networks

Existing methods regarding the influential nodes identification in complex networks usually assume that the structures of the networks are fully known. However, in many cases, knowing the full structure of one network is hard or impossible, and each participant can only obtain the partial structure of the networks. Therefore, each participant can be viewed as a private network (PN) of the original network, and they form multiple PNs. Then, one question arises: how to collaboratively identify influential nodes in multiple PNs while protecting the privacy of each PN. To this end, a secure multiparty computation ranking (SMPC-ranking) method is proposed to solve such an all-new problem based on the secure multiparty computation (SMPC) protocol, in which the SMPC protocol jointly computes a centrality function to measure the comprehensive importance of nodes or their ranking results by integrating each PN’s inputs without disclosing their inputs (i.e., the nodes’ importance). Experimental results in different networks demonstrate that, no matter what type of the centrality index is, the SMPC-ranking method can better identify the influential nodes in networks than that of the baseline method only using the structural information of one PN. What is more, our SMPC-ranking method does not violate the privacy of each PN. Therefore, the proposed method provides new insights on collaboratively identifying influential nodes in multiple PNs and has potential applications in many fields.

High Efficiency Secure Channels for a Secure Multiparty Computation Protocol Based on Signal

Secure multiparty computation (MPC) requires the messages transmitted in secure channels which can provide encryption and authorization to the messages. To implement a secure channel for MPC protocols, researchers have tried some communication protocols, such as TLS and Noise. However, these methods have some limitations. These protocols need a trusted certification authority to provide identity authorization which is difficult for an MPC protocol, and how participants manage the key of each party and how to use the key to establish communication is also a problem. A Signal protocol is an end-to-end encryption communication protocol, which is known as the most secure communication protocol in the world. Based on the Signal protocol, we implemented a signal-based secure multiparty computation protocol, which can run the MPC protocol and transmit messages through a signal-based secure channel. Compared with previous research on the MPC protocol over Signal secure channels, the new MPC client adds group communication of the Signal protocol to transmit messages, which significantly improves the communication efficiency of broadcast messages of MPC protocols. To test the communication efficiency of the new MPC client, we implemented a concrete BLS threshold signature protocol on the new client, comparing the elapsed time of key generation and signing on the new client to that on the client only using Signal end-to-end communication. According to our experiment result, we found that the new client run at least 37.48% faster than the old client on the BLS threshold signature whose number of parties ranges from 3 to 5, if the parties have sent Signal group messages to each other. The more parties in the MPC protocol, the higher the proportion of broadcast messages and the more obvious the performance improvement of the new client. Our work improves the performance of MPC secure channels based on the Signal protocol, especially for complex MPC protocols with many participants.

An Efficient Privacy-Enhancing Cross-Silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids

Federated Learning is a prominent machine learning paradigm which helps tackle data privacy issues by allowing clients to store their raw data locally and transfer only their local model parameters to an aggregator server to collaboratively train a shared global model. However, federated learning is vulnerable to inference attacks from dishonest aggregators who can infer information about clients’ training data from their model parameters. To deal with this issue, most of the proposed schemes in literature either require a non-colluded server setting, a trusted third-party to compute master secret keys or a secure multiparty computation protocol which is still inefficient over multiple iterations of computing an aggregation model. In this work, we propose an efficient cross-silo federated learning scheme with strong privacy preservation. By designing a double-layer encryption scheme which has no requirement to compute discrete logarithm, utilizing secret sharing only at the establishment phase and in the iterations when parties rejoin, and accelerating the computation performance via parallel computing, we achieve an efficient privacy-preserving federated learning protocol, which also allows clients to dropout and rejoin during the training process. The proposed scheme is demonstrated theoretically and empirically to provide provable privacy against an honest-but-curious aggregator server and simultaneously achieve desirable model utilities. The scheme is applied to false data injection attack detection (FDIA) in smart grids. This is a more secure cross-silo FDIA federated learning resilient to the local private data inference attacks than the existing works.

Efficient decision tree training with new data structure for secure multi-party computation

We propose a secure multi-party computation (MPC) protocol that constructs a secret-shared decision tree for a given secret-shared dataset. The previous MPC-based decision tree training protocol (Abspoel et al. 2021) requires $O(2^hmn log n)$ comparisons, being exponential in the tree height $h$ and with $n$ and $m$ being the number of rows and that of attributes in the dataset, respectively. The cause of the exponential number of comparisons in $h$ is that the decision tree training algorithm is based on the divide-and-conquer paradigm, where rows are padded after each split in order to hide the number of rows in the dataset. We resolve this issue via secure data structure that enables us to compute an aggregate value for every group while hiding the grouping information. By using this data structure, we can train a decision tree without padding to rows while hiding the size of the intermediate data. We specifically describes a decision tree training protocol that requires only $O(hmn log n)$ comparisons when the input attributes are continuous and the output attribute is binary. Note that the order is now linear in the tree height $h$. To demonstrate the practicality of our protocol, we implement it in an MPC framework based on a three-party secret sharing scheme. Our implementation results show that our protocol trains a decision tree with a height of 4 in 404 seconds for a dataset of $2^{20}$ rows and 11 attributes.