PAIRS: Privacy-Aware Identification and Recommendation of Spatio-Friends

Shuo Wang, Richard Sinnott, Surya Nepal

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Due to the prevalence of location-based services, it has now become possible to infer social connections between people by observing their spatial behaviors over time. Such spatial behaviors, if shared, can be utilized to identify and recommend friends for web-based social service users. However, such approaches cannot be implemented without solving two key challenges: (a) guaranteeing an individual's privacy in the shared spatiotemporal data, and (b) addressing the inherent sparseness of shared spatiotemporal data. In this paper, we propose a Privacy-Aware Identification and Recommendation of Spatio-Friends (PAIRS) approach, that can infer and recommend potential social connections by analyzing spatiotemporal information of social media users using robust privacy guarantee mechanisms. To achieve this, PAIRS constructs co-occurrence profiles using a cluster-based anchor representation to alleviate the sparseness of shared spatiotemporal information. It utilizes the diversity, time and weighted frequency-based inference to efficiently infer the strength of potential social connections from co-occurrence profile by reducing the negative impact of coincidences and thereby enhances accuracy. To tackle the privacy concerns, PAIRS sanitizes the cluster-based anchors, the location entropy values as well as the co-occurrence profile under differential privacy, including optimization mechanisms to handle trade-offs in utility and privacy. Extensive experiments are conducted with real-world datasets including both individuals' spatiotemporal data and their actual social connections. We confirm that our approach can achieve two often contradictory goals: a provable robust privacy protection for sharing data and an efficient social strength inference and spatio-friend identification mechanism. Specifically, PAIRS remains approximately 70% accuracy (precision) and 80% efficiency (recommendation potential) after perturbation.

Original languageEnglish
Title of host publicationProceedings - The 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2018) - The 12th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2018) - 2018 IEEE Trustcom/BigDataSE
EditorsKim-Kwang Raymond Choo, Yongxin Zhu, Zongming Fei, Bhavani Thuraisingham, Yang Xiang
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages12
ISBN (Electronic)9781538643884, 9781538643877
ISBN (Print)9781538643891
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2018 - New York, United States of America
Duration: 31 Jul 20183 Aug 2018
Conference number: 17th (Conference Website) (Proceedings)


ConferenceIEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) 2018
Abbreviated titleTrustCom 2018
CountryUnited States of America
CityNew York
Internet address


  • differential privacy
  • Link prediction
  • social networks
  • social tie
  • spatiotemporal

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