Secure Multiparty Computation and Secret Sharing

Cramer, Ronald; Damgrd, Ivan Bjerre; Nielsen, Jesper Buus

doi:10.1017/cbo9781107337756

Cited by 466 publications

(415 citation statements)

References 125 publications

Supporting

Mentioning

404

Contrasting

Unclassified

Order By: Relevance

“…Intuitively, the larger the filter, the lower the number of occurrences for high values of v i . This has strong implications on the privacy properties of the filter: if we consider a δ S resulting in 0 non-zero values, a person standing in that region will have (v 10 Table 3. Each plotted function represents the false positives probability for a specific area ∆ i .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Location privacy without mutual trust: The spatial Bloom filter

Calderoni

Palmieri

Maio

2015

Computer Communications

View full text Add to dashboard Cite

Access to the full text of the published version may require a subscription. Rights AbstractLocation-aware applications are one of the biggest innovations brought by the smartphone era, and are effectively changing our everyday lives. But we are only starting to grasp the privacy risks associated with constant tracking of our whereabouts. In order to continue using location-based services in the future without compromising our privacy and security, we need new, privacy-friendly applications and protocols. In this paper, we propose a new compact data structure based on Bloom filters, designed to store location information. The Spatial Bloom Filter (SBF), as we call it, is designed with privacy in mind, and we prove it by presenting two private positioning protocols based on the new primitive. The protocols keep the user's exact position private, but allow the provider of the service to learn when the user is close to specific points of interest, or inside predefined areas. At the same time, the points and areas of interest remain oblivious to the user. The two proposed protocols are aimed at different scenarios: a two-party setting, in which communication happens directly between the user and the service provider, and a three-party setting, in which the service provider outsources to a third party the communication with the user. A detailed evaluation of the efficiency and security of our solution shows that privacy can be achieved with minimal computational and communication overhead. The potential of spatial Bloom filters in terms of generality, security and compactness makes them ready for deployment, and may open the way for privacy preserving location-aware applications.

show abstract

Section: Discussionmentioning

confidence: 99%

“…We assume the parties are honest-butcurious [10], that is, the parties will follow the protocol but try to learn additional information about other parties private data.…”

Section: Privacy Definitionsmentioning

confidence: 99%

Location privacy without mutual trust: The spatial Bloom filter

Calderoni

Palmieri

Maio

2015

Computer Communications

View full text Add to dashboard Cite

show abstract

“…The main advantage is that their input stays private. SMC algorithms today enable us to do secure addition, multiplication, and comparison [25,[42][43][44]. SMC algorithms are used in various fields of science.…”

Section: Privacy Concerns In Supply Chainmentioning

confidence: 99%

“…Requirement 1 is an extension to conditions of SMC on satisfying privacy [44]. In our case it is not allowed that more information than the final result (BC) is shared.…”

Section: Requirement 1 the Artifact Should Keep The Scn Topology As mentioning

confidence: 99%

A Privacy Preserving Approach to Collaborative Systemic Risk Identification: The Use-Case of Supply Chain Networks

Garizy

Fridgen

Wederhake

2018

Security and Communication Networks

View full text Add to dashboard Cite

Globalization and outsourcing are two main factors which are leading to higher complexity of supply chain networks. Due to the strategic importance of having a sustainable network, it is necessary to have an enhanced supply chain network risk management. In a supply chain network many firms depend directly or indirectly on a specific supplier. In this regard, unknown risks of network's structure can endanger the whole supply chain network's robustness. In spite of the importance of risk identification of supply chain network, firms are not willing to exchange the structural information of their network. Firms are concerned about risking their strategic positioning or established connections in the network. The paper proposes to combine secure multiparty computation cryptography methods with risk identification algorithms from social network analysis to address this challenge. The combination enables structural risk identification of supply chain networks without endangering firms' competitive advantage.

show abstract

“…The users should not learn any additional information about the data of others than what they may infer from their own data and the functions they are computing. Various applications such as online auctions, electronic voting, and privacy preserving data mining motivate the study of MPC [6,Chapter 1].…”

Section: Introductionmentioning

confidence: 99%

How to securely compute the modulo-two sum of binary sources

Data¹,

Dey²,

Mishra³

et al. 2014

2014 IEEE Information Theory Workshop (ITW 2014)

View full text Add to dashboard Cite

Abstract-In secure multiparty computation, mutually distrusting users in a network want to collaborate to compute functions of data which is distributed among the users. The users should not learn any additional information about the data of others than what they may infer from their own data and the functions they are computing. Previous works have mostly considered the worst case context (i.e., without assuming any distribution for the data); Lee and Abbe (2014) is a notable exception. Here, we study the average case (i.e., we work with a distribution on the data) where correctness and privacy is only desired asymptotically.For concreteness and simplicity, we consider a secure version of the function computation problem of Körner and Marton (1979) where two users observe a doubly symmetric binary source with parameter p and the third user wants to compute the XOR. We show that the amount of communication and randomness resources required depends on the level of correctness desired. When zero-error and perfect privacy are required, the results of Data et al. (2014) show that it can be achieved if and only if a total rate of 1 bit is communicated between every pair of users and private randomness at the rate of 1 is used up. In contrast, we show here that, if we only want the probability of error to vanish asymptotically in blocklength, it can be achieved by a lower rate (binary entropy of p) for all the links and for private randomness; this also guarantees perfect privacy. We also show that no smaller rates are possible even if privacy is only required asymptotically.

show abstract

Secure Multiparty Computation and Secret Sharing

Cited by 466 publications

References 125 publications

Location privacy without mutual trust: The spatial Bloom filter

Location privacy without mutual trust: The spatial Bloom filter

A Privacy Preserving Approach to Collaborative Systemic Risk Identification: The Use-Case of Supply Chain Networks

How to securely compute the modulo-two sum of binary sources

Contact Info

Product

Resources

About