Kaaniche, N. orcid.org/0000-0002-1045-6445, Masmoudi, S., Znina, S. et al. (2 more authors) (2020) Privacy preserving cooperative computation for personalized web search applications. In: Proceedings of the 35th ACM/SIGAPP Symposium on Applied Computing (SAC2020). The 35th ACM/SIGAPP Symposium on Applied Computing (SAC '20), 30 Mar - 03 Apr 2020, Brno, Czech Republic. ACM Digital Library , pp. 250-258. ISBN 9781450368667
Abstract
With the emergence of connected objects and the development of Artificial Intelligence (AI) mechanisms and algorithms, personalized applications are gaining an expanding interest, providing services tailored to each single user needs and expectations. They mainly rely on the massive collection of personal data generated by a large number of applications hosted from different connected devices. In this paper, we present CoWSA, a privacy preserving Cooperative computation framework for personalized Web Search peripheral Applications. The proposed framework is multi-fold. First, it provides the empowerment to end-users to control the disclosed personal data to third parties, while leveraging the trade-off between privacy and utility. Second, as a decentralized solution, CoWSA mitigates single points of failures, while ensuring the security of queries, the anonymity of submitting users, and the incentive of contributing nodes. Third, CoWSA is scalable as it provides acceptable computation and communication costs compared to most closely related schemes.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2020 Association for Computing Machinery. This is an author-produced version of a paper subsequently published in SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | privacy; web search engines; personalized services; collaborative computation; decentralized architectures; Interest-based networks |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Dec 2019 10:25 |
Last Modified: | 06 May 2020 10:12 |
Status: | Published online |
Publisher: | ACM Digital Library |
Refereed: | Yes |
Identification Number: | 10.1145/3341105.3373947 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:154282 |