How to Hide One’s Relationships from Link Prediction Algorithms

Abstract Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by...

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Autores principales:	Marcin Waniek, Kai Zhou, Yevgeniy Vorobeychik, Esteban Moro, Tomasz P. Michalak, Talal Rahwan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/018166adb2514eef8a80daf45f31c3cb
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spelling	oai:doaj.org-article:018166adb2514eef8a80daf45f31c3cb2021-12-02T15:09:24ZHow to Hide One’s Relationships from Link Prediction Algorithms10.1038/s41598-019-48583-62045-2322https://doaj.org/article/018166adb2514eef8a80daf45f31c3cb2019-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-48583-6https://doaj.org/toc/2045-2322Abstract Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.Marcin WaniekKai ZhouYevgeniy VorobeychikEsteban MoroTomasz P. MichalakTalal RahwanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marcin Waniek Kai Zhou Yevgeniy Vorobeychik Esteban Moro Tomasz P. Michalak Talal Rahwan How to Hide One’s Relationships from Link Prediction Algorithms
description	Abstract Our private connections can be exposed by link prediction algorithms. To date, this threat has only been addressed from the perspective of a central authority, completely neglecting the possibility that members of the social network can themselves mitigate such threats. We fill this gap by studying how an individual can rewire her own network neighborhood to hide her sensitive relationships. We prove that the optimization problem faced by such an individual is NP-complete, meaning that any attempt to identify an optimal way to hide one’s relationships is futile. Based on this, we shift our attention towards developing effective, albeit not optimal, heuristics that are readily-applicable by users of existing social media platforms to conceal any connections they deem sensitive. Our empirical evaluation reveals that it is more beneficial to focus on “unfriending” carefully-chosen individuals rather than befriending new ones. In fact, by avoiding communication with just 5 individuals, it is possible for one to hide some of her relationships in a massive, real-life telecommunication network, consisting of 829,725 phone calls between 248,763 individuals. Our analysis also shows that link prediction algorithms are more susceptible to manipulation in smaller and denser networks. Evaluating the error vs. attack tolerance of link prediction algorithms reveals that rewiring connections randomly may end up exposing one’s sensitive relationships, highlighting the importance of the strategic aspect. In an age where personal relationships continue to leave digital traces, our results empower the general public to proactively protect their private relationships.
format	article
author	Marcin Waniek Kai Zhou Yevgeniy Vorobeychik Esteban Moro Tomasz P. Michalak Talal Rahwan
author_facet	Marcin Waniek Kai Zhou Yevgeniy Vorobeychik Esteban Moro Tomasz P. Michalak Talal Rahwan
author_sort	Marcin Waniek
title	How to Hide One’s Relationships from Link Prediction Algorithms
title_short	How to Hide One’s Relationships from Link Prediction Algorithms
title_full	How to Hide One’s Relationships from Link Prediction Algorithms
title_fullStr	How to Hide One’s Relationships from Link Prediction Algorithms
title_full_unstemmed	How to Hide One’s Relationships from Link Prediction Algorithms
title_sort	how to hide one’s relationships from link prediction algorithms
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/018166adb2514eef8a80daf45f31c3cb
work_keys_str_mv	AT marcinwaniek howtohideonesrelationshipsfromlinkpredictionalgorithms AT kaizhou howtohideonesrelationshipsfromlinkpredictionalgorithms AT yevgeniyvorobeychik howtohideonesrelationshipsfromlinkpredictionalgorithms AT estebanmoro howtohideonesrelationshipsfromlinkpredictionalgorithms AT tomaszpmichalak howtohideonesrelationshipsfromlinkpredictionalgorithms AT talalrahwan howtohideonesrelationshipsfromlinkpredictionalgorithms
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How to Hide One’s Relationships from Link Prediction Algorithms

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