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Privacy Preserving Collaborative Filtering from Asymmetric Randomized Encoding

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Book cover Financial Cryptography and Data Security (FC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 8975))

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Abstract

Collaborative filtering is a famous technique in recommendation systems. Yet, it requires the users to reveal their preferences, which has undesirable privacy implications. Over the years, researchers have proposed many privacy-preserving collaborative filtering (PPCF) systems using very different techniques for different settings, ranging from adding noise to the data with centralized filtering, to performing secure multi-party computation. However, either privacy protection is unsatisfactory or the computation is prohibitively expensive.

In this work, we propose a decentralized PPCF system, which enables a group of users holding (cryptographically low-entropy) profile to identify other similar users in a privacy-preserving yet very efficient way, without the help of any central server. Its core component is a novel primitive which we named as asymmetric randomized encoding (ARE). Similar to the spirt of other cryptographic primitives, it is asymmetric in the sense that, honest party could enjoy performance boost (via precomputation) with the knowledge of a profile, whilst adversary aiming to recover the hidden profile can only launch dictionary attack against each encoded profile. Thanks to the simple design of ARE, our solution is very efficient, which is demonstrated by our performance evaluation. Besides PPCF, we believe that ARE will find further applications which require a balance between privacy and efficiency.

Sherman Chow— is supported by the Early Career Scheme and the Early Career Award of the Research Grants Council, Hong Kong SAR (CUHK 439713), and Direct Grant (4055018) of the Chinese University of Hong Kong.

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Notes

  1. 1.

    We prepend dummy strings \(S_1\) and \(S_2\) to the input to instantiate two hash functions. The first \(\lambda \)-bit output of SHA3 is picked as output, i.e., \(H_i(m) = \text {SHA3}(S_i||m)[0, \ldots , \lambda -1]\) for \(i \in [1, 2]\). For \(H_1(m)\), there exists a small probability that the output is larger than \(p-1\). If that occurs, we re-hash the result until it fits.

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Authors and Affiliations

  1. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR

    Yongjun Zhao & Sherman S. M. Chow

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  1. Yongjun Zhao
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  2. Sherman S. M. Chow
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Correspondence to Yongjun Zhao or Sherman S. M. Chow .

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  1. University of Innsbruck, Innsbruck, Austria

    Rainer Böhme

  2. NTT Laboratories, Tokyo, Japan

    Tatsuaki Okamoto

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Zhao, Y., Chow, S.S.M. (2015). Privacy Preserving Collaborative Filtering from Asymmetric Randomized Encoding. In: Böhme, R., Okamoto, T. (eds) Financial Cryptography and Data Security. FC 2015. Lecture Notes in Computer Science(), vol 8975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47854-7_28

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  • DOI: https://doi.org/10.1007/978-3-662-47854-7_28

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