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

Abstract

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
Pages920-931
Number of pages12
ISBN (Electronic)9781538643884, 9781538643877
ISBN (Print)9781538643891
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventIEEE International Conference on Trust, Security and Privacy in Computing and Communications and IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) 2018 - New York, United States of America
Duration: 31 Jul 20183 Aug 2018
Conference number: 17th
http://www.cloud-conf.net/trustcom18/

Conference

ConferenceIEEE International Conference on Trust, Security and Privacy in Computing and Communications and IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) 2018
Abbreviated titleTrustCom 2018
CountryUnited States of America
CityNew York
Period31/07/183/08/18
Internet address

Keywords

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

Cite this

Wang, S., Sinnott, R., & Nepal, S. (2018). PAIRS: Privacy-Aware Identification and Recommendation of Spatio-Friends. In K-K. R. Choo, Y. Zhu, Z. Fei, B. Thuraisingham, & Y. Xiang (Eds.), Proceedings - 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 (pp. 920-931). [8456000] Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00131
Wang, Shuo ; Sinnott, Richard ; Nepal, Surya. / PAIRS : Privacy-Aware Identification and Recommendation of Spatio-Friends. Proceedings - 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. editor / Kim-Kwang Raymond Choo ; Yongxin Zhu ; Zongming Fei ; Bhavani Thuraisingham ; Yang Xiang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 920-931
@inproceedings{c47264d363514753947b47814b246a90,
title = "PAIRS: Privacy-Aware Identification and Recommendation of Spatio-Friends",
abstract = "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.",
keywords = "differential privacy, Link prediction, social networks, social tie, spatiotemporal",
author = "Shuo Wang and Richard Sinnott and Surya Nepal",
year = "2018",
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language = "English",
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booktitle = "Proceedings - 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",
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Wang, S, Sinnott, R & Nepal, S 2018, PAIRS: Privacy-Aware Identification and Recommendation of Spatio-Friends. in K-KR Choo, Y Zhu, Z Fei, B Thuraisingham & Y Xiang (eds), Proceedings - 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., 8456000, IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 920-931, IEEE International Conference on Trust, Security and Privacy in Computing and Communications and IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) 2018, New York, United States of America, 31/07/18. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00131

PAIRS : Privacy-Aware Identification and Recommendation of Spatio-Friends. / Wang, Shuo; Sinnott, Richard; Nepal, Surya.

Proceedings - 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. ed. / Kim-Kwang Raymond Choo; Yongxin Zhu; Zongming Fei; Bhavani Thuraisingham; Yang Xiang. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 920-931 8456000.

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

TY - GEN

T1 - PAIRS

T2 - Privacy-Aware Identification and Recommendation of Spatio-Friends

AU - Wang, Shuo

AU - Sinnott, Richard

AU - Nepal, Surya

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - differential privacy

KW - Link prediction

KW - social networks

KW - social tie

KW - spatiotemporal

UR - http://www.scopus.com/inward/record.url?scp=85054074607&partnerID=8YFLogxK

U2 - 10.1109/TrustCom/BigDataSE.2018.00131

DO - 10.1109/TrustCom/BigDataSE.2018.00131

M3 - Conference Paper

SN - 9781538643891

SP - 920

EP - 931

BT - Proceedings - 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

A2 - Choo, Kim-Kwang Raymond

A2 - Zhu, Yongxin

A2 - Fei, Zongming

A2 - Thuraisingham, Bhavani

A2 - Xiang, Yang

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

ER -

Wang S, Sinnott R, Nepal S. PAIRS: Privacy-Aware Identification and Recommendation of Spatio-Friends. In Choo K-KR, Zhu Y, Fei Z, Thuraisingham B, Xiang Y, editors, Proceedings - 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. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 920-931. 8456000 https://doi.org/10.1109/TrustCom/BigDataSE.2018.00131