Secure Multiparty Computation Protocols Research Articles

<inline-formula> <tex-math notation="TeX">\(m\) </tex-math></inline-formula>-Privacy for Collaborative Data Publishing

In this paper, we consider the collaborative data publishing problem for anonymizing horizontally partitioned data at multiple data providers. We consider a new type of insider attack by colluding data providers who may use their own data records (a subset of the overall data) to infer the data records contributed by other data providers. The paper addresses this new threat, and makes several contributions. First, we introduce the notion of m-privacy, which guarantees that the anonymized data satisfies a given privacy constraint against any group of up to m colluding data providers. Second, we present heuristic algorithms exploiting the monotonicity of privacy constraints for efficiently checking m-privacy given a group of records. Third, we present a data provider-aware anonymization algorithm with adaptive m-privacy checking strategies to ensure high utility and m-privacy of anonymized data with efficiency. Finally, we implement the m-privacy anonymization and verification algorithms with a trusted third party (TTP), and propose secure multiparty computation protocols for scenarios without TTP. All protocols are extensively analyzed and their security and efficiency are formally proved. Experiments on real-life datasets suggest that our approach achieves better or comparable utility and efficiency than existing and baseline algorithms while satisfying m-privacy.

A subexponential construction of graph coloring for multiparty computation

Abstract We show the first deterministic construction of an unconditionally secure multiparty computation (MPC) protocol in the passive adversarial model over black-box non-Abelian groups which is both optimal (secure against an adversary who possesses any t < n 2 $t < \frac{n}{2}$ inputs) and has subexponential complexity of construction based on coloring of planar graphs. More specifically, following the result of Desmedt et al. (2012) that the problem of MPC over non-Abelian groups can be reduced to finding a t-reliable n-coloring of planar graphs, we show the construction of such a graph which allows a path from the input nodes to the output nodes when any t-party subset is in the possession of the adversary. Unlike the deterministic constructions from Desmedt et al. (2012) our construction has subexponential complexity and is optimal at the same time, i.e., it is secure for any t < n 2 $t < \frac{n}{2}$ .

Secure Multi-party Computation based on m-out-n OT

Secure multi-party computation protocol can be realized in many ways, which based on OT construct is a useful realization. We use all but many lossy trapdoor function to design noninteractive chosen m out of n OT. Then, we design multi-party computation protocl based on the new OT. Any two participators of the participants need not execute one out of four OT. We introduce the trust (semi-honest) authority (cloud server), then design noninteractive chosen n − i out of 4(n − i) OT between participants to realize multi-party computation protocl.

A compressive sensing based secure watermark detection and privacy preserving storage framework.

Privacy is a critical issue when the data owners outsource data storage or processing to a third party computing service, such as the cloud. In this paper, we identify a cloud computing application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We then propose a compressive sensing (CS)-based framework using secure multiparty computation (MPC) protocols to address such a requirement. In our framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a CS domain to protect the privacy. During CS transformation, the privacy of the CS matrix and the watermark pattern is protected by the MPC protocols under the semi-honest security model. We derive the expected watermark detection performance in the CS domain, given the target image, watermark pattern, and the size of the CS matrix (but without the CS matrix itself). The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the CS domain is feasible. Our framework can also be extended to other collaborative secure signal processing and data-mining applications in the cloud.

A Practical, Secure, and Verifiable Cloud Computing for Mobile Systems

Cloud computing systems, in which clients rent and share computing resources of third party platforms, have gained widespread use in recent years. Furthermore, cloud computing for mobile systems (i.e., systems in which the clients are mobile devices) have too been receiving considerable attention in technical literature. We propose a new method of delegating computations of resource-constrained mobile clients, in which multiple servers interact to construct an encrypted program known as garbled circuit. Next, using garbled inputs from a mobile client, another server executes this garbled circuit and returns the resulting garbled outputs. Our system assures privacy of the mobile client's data, even if the executing server chooses to collude with all but one of the other servers. We adapt the garbled circuit design of Beaver et al. and the secure multiparty computation protocol of Goldreich et al. for the purpose of building a secure cloud computing for mobile systems. Our method incorporates the novel use of the cryptographically secure pseudo random number generator of Blum et al. that enables the mobile client to efficiently retrieve the result of the computation, as well as to verify that the evaluator actually performed the computation. We analyze the server-side and client-side complexity of our system. Using real-world data, we evaluate our system for a privacy preserving search application that locates the nearest bank/ATM from the mobile client. We also measure the time taken to construct and evaluate the garbled circuit for varying number of servers, demonstrating the feasibility of our secure and verifiable cloud computing for mobile systems.

Comparative Evaluation of Elliptic Curve Cryptography Based Homomorphic Encryption Schemes for a Novel Secure Multiparty Computation

In this paper, we focus on Elliptic Curve Cryptography based approach for Secure Multiparty Computation (SMC) problem. Widespread proliferation of data and the growth of communication technologies have enabled collaborative computations among parties in distributed scenario. Preserving privacy of data owned by parties is crucial in such scenarios. Classical approach to SMC is to perform computation using Trusted Third Party (TTP). However, in practical scenario, TTPs are hard to achieve and it is imperative to eliminate TTP in SMC. In addition, existing solutions proposed for SMC use classical homomorphic encryption schemes such as RSA and Paillier. Due to the higher cost incurred by such cryptosystems, the resultant SMC protocols are not scalable. We propose Elliptic Curve Cryptography (ECC) based approach for SMC that is scalable in terms of computational and communication cost and avoids TTP. In literature, there do exist various ECC based homomorphic schemes and it is imperative to investigate and analyze these schemes in order to select the suitable for a given application. In this paper, we empirically analyze various ECC based homomorphic encryption schemes based on performance metrics such as computational cost and communication cost. We recommend an efficient algorithm amongst several selected ones, that offers security with lesser overheads and can be applied in any application demanding privacy.

Minimum Disclosure Routing for Network Virtualization and Its Experimental Evaluation

Although the virtual collocation of service providers (SPs) on top of infrastructure providers (InPs) via network virtualization brings various benefits, we posit that operational confidentiality has not been considered in this network model. We extend and apply the Secure Multiparty Computation (SMC) protocol to solving Minimum Disclosure Routing (MDR), namely, enabling an SP to route packets without disclosing routing information to InPs. We implement the proposed MDR protocol and evaluate its performance via experiments by comparing it against the prediction based on our analytical performance model. Our study reveals that MDR can be securely achieved with marginal latency overhead with regard to the convergence time in well-engineered nonsecure routing algorithms. Our study sheds light on the path for network virtualization to be used to resolve the challenges for the ISPs of today.

Research on Privacy Preserving Data Mining Association Rules Protocol

Privacy preserving in data mining is a significant direction. There has been growing interests in private concerns for future data mining research. Privacy preserving data mining concentrates on developing accurate models without sharing precise individual data records. A privacy preserving association rule mining algorithm was introduced. This algorithm preserved privacy of individual values by computing scalar product. Then, the data mining and secure multiparty computation are briefly introduced. And proposes an implementation for privacy preserving mining protocol based secure multiparty computation protocol.

SMC Protocol for Naïve Bayes Classification over Grid Partitioned Data using Multiple UTPs

The case where data is distributed horizontally as well as vertically, it refers as grid partitioned data. SMC protocol for Naive Bayes classification over grid partitioned data is offered in this paper. Also present a solution of the Secure Multi-party Computation (SMC) problem in the form of a protocol that preserves privacy. In this system, a protocol with several Untrusted Third Parties (UTPs) is used, where there is almost impossible of privacy leakage. Multiple UTPs will calculate the model parameters for integrating the horizontally partitioned data. After that secure multiplication protocol will apply on vertical partitioned data (multiple UTPs) to classify the new tuple. The main contribution of this paper is that it shows a simple and easy calculation for developing Naive Bayes classifier for grid partitioned data. The evaluation method is simple and more efficient. General Terms SMC, Naive Bayes Classification.

Floating Point Arithmetic Protocols for Constructing Secure Data Analysis Application

A large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows participants to cooperate on various computations while retaining the privacy of their own input data, which is an ideal solution to this issue. Although there is a number of frameworks developed in SMC to meet this challenge, but they are either tailored to perform only on specific tasks or provide very limited precision. In this paper, we have developed protocols for floating point arithmetic based on secure scalar product protocols, which is re- quired in many real world applications. Our protocols follow most of the IEEE-754 standard, supporting the four fundamental arithmetic operations, namely addition, subtraction, multiplication, and division. We will demonstrate the practicality of these protocols through performing various statistical calculations that is widely used in most data analysis tasks. Our experiments show the performance of our framework is both practical and promising.

Efficient Secure Multiparty Computation Protocol for Sequencing Problem over Insecure Channel

As a powerful tool in solving privacy preserving cooperative problems, secure multiparty computation is more and more popular in electronic bidding, anonymous voting, and online auction. Privacy preserving sequencing problem which is an essential link is regarded as the core issue in these applications. However, due to the difficulties of solving multiparty privacy preserving sequencing problem, related secure protocol is extremely rare. In order to break this deadlock, this paper first presents an efficient secure multiparty computation protocol for the general privacy-preserving sequencing problem based on symmetric homomorphic encryption. The result is of value not only in theory, but also in practice.

Study on Quantum Oblivious Transfer

The Oblivious Transfer (OT) is a typical foundation agreement of secure multi-party computations. It can be used to solve the “ Millionaire” interesting question raised by the winner of the Turing Award in 1982, Mr. Yao , thus to build more complex secure multi-party computation protocol or to solve practical problems,such as electronic voting, elections, e-commerce. Using of the quantum channel and the principles of the quantum mechanics , Quantum Oblivious Transfer (QOT) can be solve the classic oblivious transfer problems. QOT can be achieved higher security and higher efficiency than the Classic Oblivious Transfer, while it also has a unique advantage in found eavesdropping. We had given a very novel QOT scheme based on three-particle entangled states. Although the use more particles , the process is better concise.

Higher-order glitch free implementation of the AES using Secure Multi-Party Computation protocols

Higher-order side channel analysis (HO-SCA) is a powerful technique against cryptographic implementations and the design of appropriate countermeasures is nowadays an important topic. In parallel, another class of attacks, called glitches attacks, have been investigated which exploit the hardware glitches phenomena occurring during the physical execution of algorithms. Some solutions have been proposed to counteract HO-SCA at any order or to defeat glitches attacks, but no work has until now focused on the definition of a sound countermeasure thwarting both attacks. We introduce in this paper a circuit model in which side-channel resistance in the presence of glitches effects can be characterized. This allows us to construct the first glitch free HO-SCA countermeasure. The new construction can be built from any Secure Multi-Party Computation protocol and, as an illustration, we propose to apply the protocol introduced by Ben-Or et al at STOC in 1988. The adaptation of the latter protocol to the context of side-channel analysis results in a completely new higher-order masking scheme, particularly interesting when addressing resistance in the presence of glitches. An application of our scheme to the AES block cipher is detailed, as well as an information theoretic evaluation of the new masking function that we call polynomial masking.

Black-Box Secret Sharing Scheme for Disjunctive Mult-i Level Access Structure

A black-box secret sharing scheme(BBSSS) differs from an ordinary linear secret sharing scheme over finite field.It works in exactly the same way over any finite Abelian group,as it only requires black-box access to group operations and to random group elements.In particular,there is no dependence on e.g.the structure of the group or its order.Until now,the efficient BBSSS for non-threshold access structure has not been constructed.In this paper,we give an approach to construct BBSSS for some non-threshold over arbitrary Abelian group by making use of the technique of monotone span scheme(MSP).First,we introduce the notation of weak MSPs and give the technique to construct the MSP based on weak MSPs.Then,we construct a pair of coprime weak MSPs by using the MSP over rational field and the MSP over finite field.Finally,we construct an efficient non-threshold BBSSS such as BBSSS for disjunctive multi-level access structure.Our new scheme can be applied to construct the new secure multi-party computation protocol over rings,linear integer secret sharing scheme,and distributed RSA signature for disjunctive multi-level access structure,and new zero-knowledge protocol.

Foreword for the special issue of selected papers from the 1st ECML/PKDD Workshop on Privacy and Security issues in Data Mining and Machine Learning

The first Workshop on Privacy and Security issues in Data Mining and Machine Learning (PSDML 2010) was organized on September 24, 2010 at Barcelona, Spain, in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Privacy and security-related aspects of data mining and machine learning have been the topic of active research during the last decade due to the existence of numerous applications with privacy and/or security requirements. Privacy issues have become a serious concern due to the increasing collection and sharing of personal d ata for purposes like data publishing or data mining. This has led to the development of privacy-preserving data mining and machine learning methods. More general security considerations arise in applications such as biometric authentication, intrusion detection and malware classification. This has led to the development of adversarial learning algorithms, while parallel work in multi-agent settings and in low regret learning algorithms has revealed interesting interplays between learning and game theory. Although significant research has so far been conducted, we are still far from addressing the numerous theoretical and practical challenges that arise in this domain. Firstly, several emerging research areas in data analysis, decision making and machine learning, require new theoretical and applied techniques for the offering of privacy or security. Secondly, there is an urgent need for learning and mining methods with privacy and security guarantees. Thirdly, there is an emerging demand for security applications such as biometric authentication and malware detection. In all cases, the strong interconnections between data mining and machine learning, cryptography and game theory, create the need for the development of multidisciplinary approaches on adversarial learning and mining problems. The aim of the PSDML workshop was to bring together scientists and practitioners who conduct research on privacy and security issues in data mining and machine learning to discuss the most recent advances in these research areas, identify open problem domains and propose possible solutions. The workshop was organized along four core subjects: (a) data privacy and security issues, (b) theoretical aspects of machine learning for security applications, (c) privacy-preserving data mining, machine learning and applications, and (d) security applications of machine learning. The present special issue contains three extended papers that have been selected among the papers presented at PSDML 2010 focusing mainly on the privacy aspects of data mining and machine learning. The first paper by Manas A. Pathak and Bhiksha Raj is titled 'Efficient Protocols for Principal Eigenvector Computation over Private Data'. It presents an efficient secure multiparty computation protocol for computing the principal eigenvector of a collection of data matrices that belong to semi-honest parties, coordinated by a semi-honest arbitrator. The protocol is augmented with randomization, data padding and oblivious transfer to conceal the information which the parties can learn from the intermediate results. The authors provide an analysis of correctness, security and efficiency of the protocol together with experimental results from a prototype implementation. The second paper by Henrik Grosskreutz, Benedikt Lemmen and Stefan Ruping is titled 'Secure Distributed Subgroup Discovery in Horizontally Partitioned Data'. The paper studies the problem of subgroup discovery and presents new secure protocols which support distributed subgroup discovery on horizontally partitioned data. The authors analyze the properties of their top-1 subgroup discovery protocol and prove that it leaks only little information, namely the size of the database and the share of positive records. They also report experimental results which demonstrate the feasibility of the approach by using a prototype implementation of the protocol. The third paper by Gerald Gavin, Julien Velcin and Philippe Aubertin is titled 'Privacy Preserving Aggregation of Secret Classifiers'. This work considers a number of parties, each having its own dataset, who build a private classifier for predicting a binary class variable. The authors develop protocols which allow combining these private classifiers in a privacy-preserving way in order to improve individual predictions. The aggregation is achieved through a secure computation of linear combinations (weighed votes) over the private classifiers. The proposed protocols are shown to be correct and private against any active polynomial adversary. We believe that the selected papers are a good representative of the research in privacy aspects of data mining and machine learning. We hope that you will enjoy reading them.

Secure multi-party computation protocol for sequencing problem

In the field of multi-party computation, an important problem is how to construct an efficient and secure multi-party computation protocol for certain specific problems. In the present study, we make use of a secret sharing scheme to construct an efficient and secure multi-party computation protocol for sequencing problems. Our protocols are perfectly secure against both a passive adversary that can corrupt at most t ⩽ (n − 1)/2 participants, and an active adversary that can corrupt at most t < n/3 participants. The simplest sequencing problem is the Millionaires’ problem.

Privacy-preserving frequent itemsets mining via secure collaborative framework

Knowledge-discovering or pattern-discovering process, such as data mining, is an important technique to discover hidden but useful information from a large volume of data. Under distributed environment, data mining task has become a challenging task due to data protection and privacy concerns. The secure multi-party computation (SMC) approach has been widely used to solve privacy-preserving data mining problems. However, generic SMC solutions are not practical from an efficiency point of view, especially when the number of parties and the size of the data are large. In view of these problems, we utilize a secure collaborative framework to facilitate the computation protocol for SMC. In this paper, we particularly consider the problem of privacy-preserving frequent itemsets mining under distributed environment. Our solution reduces the risk for central data mining and improves the efficiency of the current generic SMC solutions. Furthermore, our solution is more reliable and flexible regardless of the number of parties involved. Copyright © 2011 John Wiley & Sons, Ltd.

Multi-party computation with conversion of secret sharing

Classical results in unconditionally secure multi-party computation (MPC) protocols with a passive adversary indicate that every n-variate function can be computed by n participants, such that no set of size t < n/2 participants learns any additional information other than what they could derive from their private inputs and the output of the protocol. We study unconditionally secure MPC protocols in the presence of a passive adversary in the trusted setup (‘semi-ideal’) model, in which the participants are supplied with some auxiliary information (which is random and independent from the participant inputs) ahead of the protocol execution (such information can be purchased as a “commodity” well before a run of the protocol). We present a new MPC protocol in the trusted setup model, which allows the adversary to corrupt an arbitrary number t < n of participants. Our protocol makes use of a novel subprotocol for converting an additive secret sharing over a field to a multiplicative secret sharing, and can be used to securely evaluate any n-variate polynomial G over a field F, with inputs restricted to non-zero elements of F. The communication complexity of our protocol is O(ℓ · n 2) field elements, where ℓ is the number of non-linear monomials in G. Previous protocols in the trusted setup model require communication proportional to the number of multiplications in an arithmetic circuit for G; thus, our protocol may offer savings over previous protocols for functions with a small number of monomials but a large number of multiplications.

A secure protocol for protecting the identity of providers when disclosing data for disease surveillance.

BackgroundProviders have been reluctant to disclose patient data for public-health purposes. Even if patient privacy is ensured, the desire to protect provider confidentiality has been an important driver of this reluctance.MethodsSix requirements for a surveillance protocol were defined that satisfy the confidentiality needs of providers and ensure utility to public health. The authors developed a secure multi-party computation protocol using the Paillier cryptosystem to allow the disclosure of stratified case counts and denominators to meet these requirements. The authors evaluated the protocol in a simulated environment on its computation performance and ability to detect disease outbreak clusters.ResultsTheoretical and empirical assessments demonstrate that all requirements are met by the protocol. A system implementing the protocol scales linearly in terms of computation time as the number of providers is increased. The absolute time to perform the computations was 12.5 s for data from 3000 practices. This is acceptable performance, given that the reporting would normally be done at 24 h intervals. The accuracy of detection disease outbreak cluster was unchanged compared with a non-secure distributed surveillance protocol, with an F-score higher than 0.92 for outbreaks involving 500 or more cases.ConclusionThe protocol and associated software provide a practical method for providers to disclose patient data for sentinel, syndromic or other indicator-based surveillance while protecting patient privacy and the identity of individual providers.

Resource Fairness and Composability of Cryptographic Protocols

We introduce the notion of resource-fair protocols. Informally, this property states that if one party learns the output of the protocol, then so can all other parties, as long as they expend roughly the same amount of resources. As opposed to previously proposed definitions related to fairness, our definition follows the standard simulation paradigm and enjoys strong composability properties. In particular, our definition is similar to the security definition in the universal composability (UC) framework, but works in a model that allows any party to request additional resources from the environment to deal with dishonest parties that may prematurely abort. In this model we specify the ideally fair functionality as allowing parties to “invest resources” in return for outputs, but in such an event offering all other parties a fair deal. (The formulation of fair dealings is kept independent of any particular functionality, by defining it using a “wrapper.”) Thus, by relaxing the notion of fairness, we avoid a well-known impossibility result for fair multi-party computation with corrupted majority; in particular, our definition admits constructions that tolerate arbitrary number of corruptions. We also show that, as in the UC framework, protocols in our framework may be arbitrarily and concurrently composed. Turning to constructions, we define a “commit-prove-fair-open” functionality and design an efficient resource-fair protocol that securely realizes it, using a new variant of a cryptographic primitive known as “time-lines.” With (the fairly wrapped version of) this functionality we show that some of the existing secure multi-party computation protocols can be easily transformed into resource-fair protocols while preserving their security.