Advances and impact of secure function evaluation

Kolesnikov, Vladimir

doi:10.1002/bltj.20396

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…The special case of 2-party SMC, known as Secure Function Evaluation (SFE), is an important subcase [24] that is (different from but) closely related to the C-CBPS model. Research in SMC started nearly 3 decades ago with the path-breaking work of Yao, [40], and is broadly divided into two classes of protocols -those that are computationally secure (i.e.…”

Section: Related Workmentioning

confidence: 99%

Secure and scalable match: overcoming the universal circuit bottleneck using group programs

Krishnan¹,

Sundaram

2015

Proceedings on Privacy Enhancing Technologies

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Abstract:Confidential Content-Based Publish/Subscribe (C-CBPS) is an interaction model that allows parties to exchange content while protecting their security and privacy interests. In this paper we advance the state of the art in C-CBPS by showing how all predicate circuits in NC 1 (logarithmic-depth, bounded fan-in) can be confidentially computed by a broker while guaranteeing perfect information-theoretic security. Previous work could handle only strictly shallower circuits (e.g. those with depth O( log 2 n)). We present three protocols-UGP-Match, FSGP-Match and OFSGP-Match-based on 2-decomposable randomized encodings of group programs for circuits in NC 1 . UGP-Match is conceptually simple and has a clean proof of correctness but its running time is a polynomial with a high exponent and hence impractical. FSGP-Match uses a "fixed structure" construction that reduces the exponent drastically and achieves efficiency and scalability. OFSGP-Match optimizes the group programs further to shave off a linear factor.

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Section: Related Workmentioning

confidence: 99%

Secure and scalable match: overcoming the universal circuit bottleneck using group programs

Krishnan¹,

Sundaram

2015

Proceedings on Privacy Enhancing Technologies

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“…Under the described setting, for security reasons, the agents may want to ensure that their information is not disclosed. This privacy requirement leads to what is called secure multi-party computation, the problem of a set of agents computing an agreed-upon function of their initial states in a private way [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

Ramos¹,

Aguiar²,

Kar³

et al. 2021

Preprint

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Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. Yet, it can raise privacy concerns in situations where the agents' states contain sensitive information. In this paper, we propose a novel (noiseless) privacy preserving distributed algorithms for multi-agent systems to reach an average consensus. The main idea of the algorithms is that each agent runs a (small) network with a crafted structure and dynamics to form a network of networks (i.e., the connection between the newly created networks and their interconnections respecting the initial network connections). Together with a re-weighting of the dynamic parameters dictating the inter-agent dynamics and the initial states, we show that it is possible to ensure that the value of each node converges to the consensus value of the original network. Furthermore, we show that, under mild assumptions, it is possible to craft the dynamics such that the design can be achieved in a distributed fashion. Finally, we illustrate the proposed algorithm with examples.

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“…A distributed network reaches consensus when each node exchanges its state with its neighboring nodes, with the initial states of all the nodes broadcasted to the whole network [15]. This is an unwanted property in some cases.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Consensus over a Distributed Network against Eavesdropping Attacks

Zhou

Yang

2019

Electronics

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Motivated by the increasing risk of data leaks in distributed networks, we consider the privacy-preserving problem in a consensus network in the presence of an eavesdropper who is able to intercept the data transmitted on the network. First, we introduce a consensus protocol with privacy-preserving function, and analyze its convergence and its privacy-preserving effect. Second, we propose a criterion to measure the degree of network privacy leaks in the existence of the eavesdropper. Particularly, we consider the networks with ring topology and small-world topology, where we find a suboptimal eavesdropping strategy that maximizes the probability of privacy leaks. Finally, we verify all the derived results by numerical examples.Researches using cryptography are often involved with quantification and cypher keys [19][20][21][22]. Kishida in [19] proposed a real-time signal encryption method by combining a quantizer with the Paillier cryptosystem, which ensures the privacy of the recursion of the consensus protocol with a length-reduced key. The reduce of the length of the key is due to the changing sensitivity of the quantizer which maps real-valued states into integers. Guo et al. in [20] encoded the information with authority traceability using elliptic curve based chameleon hashing. Yang et al. in [21] introduced homomorphic encryption into a distributed projected gradient-based algorithm to reach privacy preserving distributed optimization. Different from the cryptography methods, in the studies which used intensionally generated noises, properly conceived noises were added into the transmitted data to confuse the eavesdropper from directly reading the data [23][24][25][26]. Huang et al. proposed a privacy-preserving protocol by adding independent and exponentially decaying Laplacian noise in the process of consensus update [23]. Manitara et al. in [24] also proposed a noise-adding protocal to achieve privacy preserving average consensus. However, different from the independent true random Laplacian noise in [23], the noise added in [24] is a pseudo-random sequence with finite steps, within which the sum the the sequence is fixed to zero to ensure unbiased average consensus. Similar works has been done by Mo et al. [25] and He et al. [26]. In [25], the added noise is conceived as a linear combination of a standard normal distributed random sequence with time-varying coefficients, which also ensures exact average consensus. In [26], the distribution of the added noise is not fixed, but a criterion is proposed to measure the privacy-preserving performance. Under the criterion, the optimal noise distribution is obtained. In [27,28], distributed privacy preserving consensus is used in cooperative driving and vehicular networks, which improves driving security, enhances infrastructure utilization, reduces fuel consumption and at the same time protect the vehicle privacy in platooning applications.In most of the above mentioned privacy preserving protocols, a fact is that, if the neighboring set of the nth node c...

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Advances and impact of secure function evaluation

Abstract: Secure function evaluation (SFE) is an area of cryptography

Cited by 8 publications

References 4 publications

Secure and scalable match: overcoming the universal circuit bottleneck using group programs

Secure and scalable match: overcoming the universal circuit bottleneck using group programs

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

Privacy-Preserving Consensus over a Distributed Network against Eavesdropping Attacks

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