TY - GEN
T1 - Privacy-Preserving Friend Recommendation in an Integrated Social Environment
AU - Uplavikar, Nitish M.
AU - Vaidya, Jaideep
AU - Lin, Dan
AU - Jiang, Wei
N1 - Funding Information:
Acknowledgments. Research reported in this publication was supported by the National Science Foundation under awards CNS-1564034 and the National Institutes of Health under awards R01GM118574 and R35GM134927. The content is solely the responsibility of the authors and does not necessarily represent the official views of the agencies funding the research.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Ubiquitous Online Social Networks (OSN)s play a vital role in information creation, propagation and consumption. Given the recent multiplicity of OSNs with specially accumulated knowledge, integration partnerships are formed (without regard to privacy) to provide an enriched, integrated and personalized social experience. However, given the increasing privacy concerns and threats, it is important to develop methods that can provide collaborative capabilities while preserving user privacy. In this work, we focus on friend recommendation systems (FRS) for such partnered OSNs. We identify the various ways through which privacy leaks can occur, and propose a comprehensive solution that integrates both Differential Privacy and Secure Multi-Party Computation to provide a holistic privacy guarantee. We analyze the security of the proposed approach and evaluate the proposed solution with real data in terms of both utility and computational complexity.
AB - Ubiquitous Online Social Networks (OSN)s play a vital role in information creation, propagation and consumption. Given the recent multiplicity of OSNs with specially accumulated knowledge, integration partnerships are formed (without regard to privacy) to provide an enriched, integrated and personalized social experience. However, given the increasing privacy concerns and threats, it is important to develop methods that can provide collaborative capabilities while preserving user privacy. In this work, we focus on friend recommendation systems (FRS) for such partnered OSNs. We identify the various ways through which privacy leaks can occur, and propose a comprehensive solution that integrates both Differential Privacy and Secure Multi-Party Computation to provide a holistic privacy guarantee. We analyze the security of the proposed approach and evaluate the proposed solution with real data in terms of both utility and computational complexity.
KW - Differential privacy
KW - Friend recommendation
KW - OSNs
KW - Secure multiparty computation
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U2 - 10.1007/978-3-030-65610-2_8
DO - 10.1007/978-3-030-65610-2_8
M3 - Conference contribution
AN - SCOPUS:85097847263
SN - 9783030656096
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 117
EP - 136
BT - Information Systems Security - 16th International Conference, ICISS 2020, Proceedings
A2 - Kanhere, Salil
A2 - Patil, Vishwas T
A2 - Sural, Shamik
A2 - Gaur, Manoj S
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Information Systems Security, ICISS 2020
Y2 - 16 December 2020 through 20 December 2020
ER -