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Effects of inconsistently masked data using RPT on CF with privacy

Published: 11 March 2007 Publication History

Abstract

Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed so far, users disguise their ratings in the same way to achieve consistently perturbed data. However, since users might have different levels of concerns about their privacy, the customers might decide to perturb their private data differently, which causes inconsistently masked data. How, then, can e-companies present referrals using such data and how can inconsistent data disguising affect accuracy and privacy?

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cover image ACM Conferences
SAC '07: Proceedings of the 2007 ACM symposium on Applied computing
March 2007
1688 pages
ISBN:1595934804
DOI:10.1145/1244002
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 11 March 2007

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Author Tags

  1. CF
  2. RPT
  3. accuracy
  4. inconsistently perturbed data
  5. privacy

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  • (2016)Randomization-based Privacy-preserving Frameworks for Collaborative FilteringProcedia Computer Science10.1016/j.procs.2016.08.09196:C(33-42)Online publication date: 1-Oct-2016
  • (2016)Reconstructing rated items from perturbed dataNeurocomputing10.1016/j.neucom.2016.05.014207:C(374-386)Online publication date: 26-Sep-2016
  • (2012)Privacy-preserving SOM-based recommendations on horizontally distributed dataKnowledge-Based Systems10.1016/j.knosys.2012.02.01333(124-135)Online publication date: 1-Sep-2012
  • (2010)Providing Private Recommendations on Personal Social NetworksAdvances in Intelligent Web Mastering - 210.1007/978-3-642-10687-3_11(117-125)Online publication date: 2010
  • (2007)Privacy-preserving eigentaste-based collaborative filteringProceedings of the Security 2nd international conference on Advances in information and computer security10.5555/1778902.1778918(169-184)Online publication date: 29-Oct-2007
  • (2007)Privacy-Preserving Eigentaste-Based Collaborative FilteringAdvances in Information and Computer Security10.1007/978-3-540-75651-4_12(169-184)Online publication date: 2007

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