Towards Empirical Aspects of Secure Scalar Product

Wang, I-Cheng; Shen, Chih-Hao; Hsu, Tsan-sheng; Liao, Churn-Chung; Wang, Dawei; Zhan, Justin

doi:10.1109/isa.2008.78

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…In addition, empirical studies and a real-world case study are conducted to evaluate this implementation framework [8], [9], [10]. However, to implement a new protocol using their framework, the user has to write Ruby code for each party and follows many detailed conventions, including the building block, data communication and additive sharing ones.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Indeed, we have proposed an information theoretical framework to quantitatively measure the amount of privacy leakage in a protocol [7], and conducted empirical studies on such information theoretically secure protocols developed in a two-party setting [8], [9]. Besides, we have developed a distributed SMC framework for implementing secure protocols with the help of a commodity server in the Ruby language.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Scripting Language for Automating Secure Multiparty Computation

Chen

Hsu²,

Liau³

et al. 2013

2013 Eighth Asia Joint Conference on Information Security

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In this paper, we report our work on a scripting language for automating the development of complex protocols for a commodity-based approach to SMC. Our scripting language models the participating parties in a peer-to-peer symmetric manner that each party holds their private data as well as any intermediate results jointly. We propose a threelevel security attributes, namely public, private, and shared, that users can employ to express their security requirements by associating variables with these attributes. Furthermore, these security attributes also direct how our compiler should perform security check as well as code generation. We show how complex protocols can be expressed in our scripting language and how the compiler works to generates code exploiting the distributed SMC runtime environment we developed.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Scripting Language for Automating Secure Multiparty Computation

Chen

Hsu²,

Liau³

et al. 2013

2013 Eighth Asia Joint Conference on Information Security

Self Cite

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“…Alice (but not Bob) is to get the result of X · Y + r , where r is a random number known by Bob only. Various solutions have been proposed in the literature (Amirbekyan and Estivill-Castro 2007;Wang et al 2008;Yang et al 2006). Here, we propose another protocol based on the commodity server architecture.…”

Section: Secure Scalar Product Protocol (Sspp)mentioning

confidence: 99%

Private predictions on hidden Markov models

Polat

Renckes

et al. 2010

Artif Intell Rev

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Hidden Markov models (HMMs) are widely used in practice to make predictions. They are becoming increasingly popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, signal processing, and so on. However, traditional HMMs do not allow people and model owners to generate predictions without disclosing their private information to each other. To address the increasing needs for privacy, this work identifies and studies the private prediction problem; it is demonstrated with the following scenario: Bob has a private HMM, while Alice has a private input; and she wants to use Bob's model to make a prediction based on her input. However, Alice does not want to disclose her private input to Bob, while Bob wants to prevent Alice from deriving information about his model. How can Alice and Bob perform HMMs-based predictions without violating their privacy? We propose privacy-preserving protocols to produce predictions on HMMs without greatly exposing Bob's and Alice's privacy. We then analyze our schemes in terms of accuracy, privacy, and performance. Since they are conflicting goals, due to privacy concerns, it is expected that accuracy or performance might degrade. However, our schemes make it possible for Bob and Alice to produce the same predictions efficiently while preserving their privacy.

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“…It has been shown that the commodity-based scalar product protocol is a very efficient approach compared to existing benchmarks [12]. The scalar product approach is based on the above scalar product protocol.…”

Section: Theorem 1 (Secure Two-party Product Protocol) π Is An Informmentioning

confidence: 99%

Towards efficient privacy-preserving collaborative recommender systems

Zhan

Wang

Hsieh

et al. 2008

2008 IEEE International Conference on Granular Computing

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Recommender systems use various types of information to help customers find products of personalized interest. To increase the usefulness of recommender systems in certain circumstances, it could be desirable to merge recommender system databases between companies, thus expanding the data pool. This can lead to privacy disclosure hazards that this paper addresses byconstructing an efficient privacy-preserving collaborative recommender system based on the scalar product protocol.

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Towards Empirical Aspects of Secure Scalar Product

Cited by 13 publications

References 21 publications

A Scripting Language for Automating Secure Multiparty Computation

A Scripting Language for Automating Secure Multiparty Computation

Private predictions on hidden Markov models

Towards efficient privacy-preserving collaborative recommender systems

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