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Title: Lightweight Privacy-Preserving Peer-to-Peer Data Integration
Citation Type: Working Paper
Publication Year: 2011
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Abstract: Peer Data Management System (PDMS) is an attractive solution for managing distributed heterogeneous information. When a peer (client) requests to retrieve data from another peer(server) with a diff erent schema, translations of the query and its answers are done by intermediate peers (translators). There are two privacy issues in this translation process: (i)answer privacy: no unauthorized parties (including the translators) should know the query result; (ii) mapping privacy: the translation can be a paid service, thus the schema and thevalue mappings used by the translators to perform the translation should not be revealed to other peers. PPP [7], based on commutative encryption, is the fi rst protocol proposed to support privacy-preserving querying in PDMS. However, PPP suff ers from several shortcomings. First, PPP cannot satisfy the requirement of answer privacy (an attack is given in this paper). We show that this issue can be solved by adopting another cryptographic technique called oblivious transfer. Second, PPP adopts a weaker notion for mapping privacy, which allows the client peer observe certain mappings done by translators. In this paper, we stick to the strict requirement that no party can observe the mappings of other peers and develop two lightweight protocols: a simple method based on serial translation and a more complex one that supports parallel translation. Furthermore, we consider a stronger adversary model where there may be collusions among peers and propose an efficient protocol that guards against collusions. We conduct an experimental study on the performance of the proposed protocols using both real and synthetic data. The results show that the proposed protocols not only achieve a better privacy guarantee than PPP, but they are also significantly more efficient, because they do not rely on expensive cryptographic operations.
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Authors: Yiu, S.M.; Cheung, David W.; Mamoulis, Nikos; Zhang, Ye; Wong, Wai-Kit
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Publication Number: TR-2011-12
Institution: Hong Kong University
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Data Collections: IPUMS USA
Topics: Methodology and Data Collection, Other
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