General Secure Multiparty Computation Research Articles

Secure Decentralized Image Classification With Multiparty Homomorphic Encryption

Decentralized image classification plays a key role in various scenarios due to its attractive properties, including tolerating high network latency and less prone to single-point failures. Unfortunately, training such a decentralized image classification model is more vulnerable to data privacy leaks compared to other distributed training frameworks. Existing efforts exclusively use differential privacy as the cornerstone to alleviate the threat to data privacy. However, differential privacy is implemented at the expense of accuracy, which goes against our motivation for designing an image classification model without loss of accuracy. To address this problem, we propose D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">first</i> secure and efficient decentralized training framework with lossless precision. Inspired by the latest developments in the homomorphic encryption technology, we design a multiparty version of Brakerski-Fan-Vercauteren (BFV), one of the most advanced cryptosystems, and use it to implement private gradient updates of users’ local models. D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE can reduce the communication complexity of general Secure Multiparty Computation (MPC) tasks from quadratic to linear in the number of users, making it very suitable and scalable for large-scale decentralized learning systems. Moreover, D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE provides strict semantic security protection even if the majority of users are dishonest with collusion. We conduct extensive experiments on MNIST, CIFAR-10, and ImageNet to demonstrate the superiority of D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE. Experimental results show that D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -MHE achieves up to 5.5× reduction in computation overhead, and at least 12× reduction in communication overhead compared to existing schemes.

Secure multi-party functional dependency discovery

Data profiling is an important task to understand data semantics and is an essential pre-processing step in many tools. Due to privacy constraints, data is often partitioned into silos, with different access control. Discovering functional dependencies (FDs) usually requires access to all data partitions to find constraints that hold on the whole dataset. Simply applying general secure multi-party computation protocols incurs high computation and communication cost. This paper formulates the FD discovery problem in the secure multi-party scenario. We propose secure constructions for validating candidate FDs, and present efficient cryptographic protocols to discover FDs over distributed partitions. Experimental results show that solution is practically efficient over non-secure distributed FD discovery, and can significantly outperform general purpose multi-party computation frameworks. To the best of our knowledge, our work is the first one to tackle this problem.

BFR-MPC: A Blockchain-Based Fair and Robust Multi-Party Computation Scheme

In a general secure multi-party computation (MPC) protocol, two or more participants who do not trust each other, use their respective secret inputs to calculate a joint function in a distributed environment without a centralized organization. They can get correct outputs on the premise of ensuring privacy and independence of input. In this paper, to solve the problem of fairness and robustness in MPC, a blockchain-based multi-party computation scheme (BFR-MPC) was proposed. The blockchain maintains an open reputation system for parties as a public ledger where a more reputable party has a greater chance to be selected. The block height is used as a trusted timestamp. In each round, parties must send the correct information before the deadline. In our scheme, all parties are considered to be foresighted, and an incentive mechanism encourages parties to cooperate rather than deviate from the protocol. Because of non-cooperative parties will be immediately expelled from the protocol and will be penalized financially, the proposed scheme is robust. The penalty will be used to reward honest parties. We also proved the fairness of our scheme through Game Theory. The comparison results of the proposed scheme with other schemes show that it is a more practical scheme for MPC with high fairness and robustness.

Distributed RK- Secure Sum Protocol for Privacy Preserving

Secure multi party computation allows several parties to compute some function of their inputs without disclosing the actual input to one another. Secure sum computation is an easily understood example and the component of the various secure multi party computation solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a protocol with more security, when a group of the computing parties want to know the data of some other party. I. INTRODUCTION The huge growth of the Internet and its easy access by common man created opportunities for joint computations by multiple parties. All the participating parties for the sake of their mutual benefit want to compute the common function of their inputs but at the same time they are worried about the privacy of their data. This subject of the information security is called secure multi party computation. This subject has two goals; one is the privacy of the individual data inputs and another is the correctness of the result. Mainly two models exist in the literature for the analysis of the secure multi party computation problems. Ideal model of the secure multi party computation uses a Trusted Third Party apart from the participating parties. Parties supply their inputs to the Trusted Third Party. Computation of the function is done by the Trusted Third Party and then the result is sent to all the parties. In this paradigm the trustworthiness of the Trusted Third Party is critically important because if the Trusted Third Party turns corrupt, it can supply the private inputs of one party to others. But it is extensively used model of the secure multi party computation due to its easy implementation and the protocols available which prevent the Trusted Third Party to act maliciously. Real model of the secure multi party computation does not use any Trusted Third Party but the parties themselves agree on some protocol for the computation. The parties behavior in the secure multi party computation is important to consider. An honest party follows the protocol and respects the privacy of other parties. A semi honest party follows the protocol but also tries to learn other information than the result. The corrupt party neither follows the protocol nor respects the privacy of other parties. Different protocols are needed for different secure multi party computation models and the behavior of the party. Solutions are available for secure multi party computation problems using cryptographic techniques, randomization techniques and anonymization methods. The subject of secure multi party computation has been evolved from two party comparison problems (1) to multiparty image template matching problems. Many specific secure multi party computation problems have been defined and analyzed by researchers like Private Information Retrieval, Selective Function Evaluation, Privacy-Preserving Database Query, Privacy-Preserving Geometric Computation, Privacy- Preserving Statistical Analysis, Privacy-Preserving Intrusion Detection and Privacy-Preserving Cooperative Scientific Computation. Based on these general secure multi party computation problems many real life applications have been emerged like Privacy- Preserving Electronic Voting, Privacy-Preserving Bidding and Auctions, Privacy-Preserving Social Network Analysis, Privacy-Preserving Signature and Face Detection, Etc. Secure sum computation problem of secure multi party computation can be defined as: How multiple parties can compute the sum of their input values without disclosing actual values to one another. Secure sum can work as the tool for the secure multi party computation solutions in the privacy preserving distributed data mining problems (2). We proposed a secure sum protocol using random numbers (2). In this paper, we proposed a novel changing neighbors approach Distributed RK secure sum protocol for achieving more security in case a group of the parties collude to know the private data of some other party. 1.1 Distributed Database Partition: - Database is divided into three main portioning horizontal partition, vertical partition and hybrid partition. 1.1.1 Horizontal Partitioning: - Horizontal partitioning divides the whole database into the number of small database according to the row splitting. So that the execution of query will be very fast as well as we will be able to provide more privacy to the portioned database.

Efficient privacy-preserving similar document detection

Similar document detection plays important roles in many applications, such as file management, copyright protection, plagiarism prevention, and duplicate submission detection. The state of the art protocols assume that the contents of files stored on a server (or multiple servers) are directly accessible. However, this makes such protocols unsuitable for any environment where the documents themselves are sensitive and cannot be openly read. Essentially, this assumption limits more practical applications, e.g., detecting plagiarized documents between two conferences, where submissions are confidential. We propose novel protocols to detect similar documents between two entities where documents cannot be openly shared with each other. The similarity measure used can be a simple cosine similarity on entire documents or on document fragments, enabling detection of partial copying. We conduct extensive experiments to show the practical value of the proposed protocols. While the proposed base protocols are much more efficient than the general secure multiparty computation based solutions, they are still slow for large document sets. We then investigate a clustering based approach that significantly reduces the running time and achieves over 90% of accuracy in our experiments. This makes secure similar document detection both practical and feasible.

Secure Two-party Protocols for Point Inclusion Problem

It is well known that, in theory, the general secure multiparty computation problem is solvable using circuit evaluation protocols. However, the communication complexity of the resulting protocols depend on the size of the circuit that expresses the functionality to be computed and hence can be impractical. Hence special solutions are needed for specific problems for efficiency reasons. The point inclusion problem in computational geometry is a special multiparty computation and has got many applications. Previous protocols for the secure point inclusion problem are not adequate. In this paper we modify some known solutions to the point inclusion problem in computational geometry to the frame work of secure two-party computation.

Homomorphic encryption and secure comparison

We propose a protocol for secure comparison of integers based on homomorphic encryption. We also propose a homomorphic encryption scheme that can be used in our protocol, makes it more efficient than previous solutions, and can also be used as the basis of efficient and general secure Multiparty Computation (MPC). We show how our comparison protocol can be used to improve security of online auctions, and demonstrate that it is efficient enough to be used in practice. For comparison of 16 bits numbers with security based on 1024 bits RSA (executed by two parties), our implementation takes 0.28 sec including all computation and communication. Using precomputation, one can save a factor of roughly 10.

Secure Communication in Minimal Connectivity Models

Problems of secure communication and computation have been studied extensively in network models. In this work we ask what is possible in the information-theoretic setting when the adversary is very strong (Byzantine) and the network connectivity is very low (minimum needed for crash-tolerance). We concentrate on a new model called ``multicast lines,'' and show a sizable gap between the connectivity required for perfect security and for almost perfect security. Our results also have implications to the commonly studied simple channel model and to general secure multiparty computation.

Span Programs and General Secure Multi-Party Computation

The contributions of this paper are three-fold. First, as an abstraction of previously proposed cryptographic protocols we propose two cryptographic primitives: homomorphic&lt;br /&gt;shared commitments and linear secret sharing schemes with an additional multiplication property. We describe new constructions for general secure multi-party computation protocols, both in the cryptographic and the information-theoretic (or secure&lt;br /&gt;channels) setting, based on any realizations of these primitives.&lt;br /&gt;Second, span programs, a model of computation introduced by Karchmer and Wigderson, are used as the basis for constructing new linear secret sharing schemes, from which the two above-mentioned primitives as well as a novel verifiable secret sharing scheme can efficiently be realized. Third, note that linear secret sharing schemes can have arbitrary (as opposed to&lt;br /&gt;threshold) access structures. If used in our construction, this yields multi-party protocols secure against general sets of active adversaries, as long as in the cryptographic (information-theoretic) model no two (no three) of these potentially misbehaving player sets cover the full player set. This is a strict generalization of the threshold-type adversaries and results previously considered in the literature. While this result is new for the cryptographic model, the result for the information-theoretic model was previously proved by Hirt and Maurer. However, in addition to providing an independent proof, our protocols are not recursive and have the potential of being more efficient.