Private Distributed Scalar Product Protocol With Application To Privacy-Preserving Computation of Trust

Yao, Danfeng; Tamassia, Roberto; Proctor, Seth

doi:10.1007/978-0-387-73655-6_1

Cited by 20 publications

(23 citation statements)

References 33 publications

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“…If a resource owner thinks organization A is more reputable than organization B, the resource owner specifies a higher point value for a credential issued by A than the one issued by B. This idea has been explored in a recent paper that quantitatively studies the connections between computational trust/reputation models with point values in point-based trust management [Yao et al 2007]. The article also discusses the application of such models in privacy-preserving location systems [Yao et al 2007].…”

Section: Point-based Trust Managementmentioning

confidence: 97%

Private Information

Yao

Frikken

Atallah

et al. 2008

ACM Trans. Inf. Syst. Secur.

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This article studies the notion of quantitative policies for trust management and gives protocols for realizing them in a disclosure-minimizing fashion. Specifically, Bob values each credential with a certain number of points, and requires a minimum total threshold of points before granting Alice access to a resource. In turn, Alice values each of her credentials with a privacy score that indicates her degree of reluctance to reveal that credential. Bob's valuation of credentials and his threshold are private. Alice's privacy-valuation of her credentials is also private. Alice wants to find a subset of her credentials that achieves Bob's required threshold for access, yet is of as small a value to her as possible. We give protocols for computing such a subset of Alice's credentials without revealing any of the two parties' above-mentioned private information. Furthermore, we develop a fingerprint method that allows Alice to independently and easily recover the optimal

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Section: Point-based Trust Managementmentioning

confidence: 97%

Private Information

Yao

Frikken

Atallah

et al. 2008

ACM Trans. Inf. Syst. Secur.

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“…[17] put forward the protocol based on homomorphic encryption computation. For the special case l = n + 1, Yao et al presented a multi-party computation protocol for computing weighted trust values [4]. This protocol is split in two phases: an homomorphic encryption com putation and a private multi-party summation protocol [IS].…”

Section: A Private Distributed Scalar Product Protocolmentioning

confidence: 99%

“…This protocol is split in two phases: an homomorphic encryption com putation and a private multi-party summation protocol [IS]. In [19], Melchor proved that the protocol of [4] is not secure in the semi-honest model by showing that it is not resistant to collusion attacks, and they proposed to use a superposed sending round as an alteruative to the multi-party summation protocol, which results in better security properties and in a reduction of the communication costs.…”

Section: A Private Distributed Scalar Product Protocolmentioning

confidence: 99%

“…However, previous works pay little attention to privacy preserving in the computation of trust value. Yao et al proposed a distributed scalar product protocol with appli cation to privacy-preserving computation of trust value [4]. However this protocol is based on homomorphic encryption computation, due to the computation restriction of WSN s, it is not suitable for WSN s.…”

Section: Introductionmentioning

confidence: 98%

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Privacy preserving computation of trust-value in wireless sensor networks

Yuan

Huang

Yangt

2011

2011 IEEE 3rd International Conference on Communication Software and Networks

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The traditional cryptographic mechanisms are not enough for wireless sensor networks (WSNs) when the control of sensor nodes are taken by an interior adversary. Trust manage ment system can solve this problem efficiently, and enhance the security and reliability of WSNs. One of the key problems in trust management system is computing trust values of nodes. However, previous works pay little attention to preserving data privacy during computing trust values. This paper focus on the problem that how to protect the data privacy while computing trust values, and proposes a privacy preserving trust value computation (PPTC) protocol to solve this problem. We develop a new private distributed scalar product protocol based on semi-honest third party to achieve the PPTC protocol. A great advantage of PPTC protocol is that it does not need any encryption or decryption operations, and the computation and communication overheads is low to O(n). What's more, we analyze the correctness and security of the PPTC protocol and prove that the protocol can resist to collusion of up to n -1 users.

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“…In [13], authors proposed a general protocol for privacy preserving multiparty scalar product computations which be used for obtaining trust values from private recommendations. They also proposed credential based trust model where the trustworthiness of the user is computed based on his/her affiliations and role assignments.…”

Section: Privacy Preserving Association Rule Mining For Vertically Pamentioning

confidence: 99%

Privacy Preserving Association Rule Mining in Vertically Partitioned Databases

Lakshmi¹,

Rani²

2012

IJCA

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Data mining techniques are useful to discover hidden patterns from the large databases. Association rule mining is one of the important data mining techniques to discover relationships between items or item sets. In many organizations the database may exist in centralized or in distributed environment.In distributed environment, database may be partitioned in different ways such as horizontally partitioned, vertically partitioned and mixed mode which consists of both horizontal and vertical partitioning methods. The sites in the distributed environment interested to find association rules by participating themselves in the mining process without disclosing their individual private data/information. In this paper, a new model is proposed to find association rules by satisfying the privacy constraints for vertically partitioned databases at n number of sites along with data miner. This model adopts cryptography techniques such as encryption, decryption techniques and scalar product technique to find association rules efficiently and securely for vertically partitioned databases.

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Private Distributed Scalar Product Protocol With Application To Privacy-Preserving Computation of Trust

Cited by 20 publications

References 33 publications

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Privacy preserving computation of trust-value in wireless sensor networks

Privacy Preserving Association Rule Mining in Vertically Partitioned Databases

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