Consent Recommender System: A Case Study on LinkedIn Settings

Consent Recommender System: A Case Study on LinkedIn Settings
Rosni K V, Manish Shukla, Vijayanand Banahatti, Sachin Lodha
Central Europe Workshop Proceedings, 18 March 2019; 2335 pp 53-60
Privacy is an increasing concern in the digital world, especially when it has become a common knowledge that even high profile enterprises process data without data-subject’s consent. In certain cases where data-subject’s consent was taken, it was not linked to the proper purpose of processing. To address this growing concern, newer privacy regulations and laws are emerging to empower a data-subject with informed and explicit consent through which she can allow or revoke usage of her personal data. However, it has been shown that privacy self-management does not provide the expected results. This is mainly due to information overload as data-subjects use multiple services entailing variety of purposes, and hence, resulting in a very large number of consent requests. This may lead to consent fatigue as data-subject is now expected to provide informed consent for each associated purpose. The consent fatigue in data-subjects can lead to either incorrect decision making or opting for default values provided by the enterprise, and thus, defeating the purpose of new data privacy regulations. In this work, we discuss the factors influencing the informed consent of a data-subject. Further, we propose a ‘consent recommender system’ based on Factorization Machines (FMs) to assist the data-subject and thereby avoiding consent fatigue. Our consent recommender system effectively models the interaction between the different factors which influence a data-subject’s informed consent. We discuss how this setup extends for cold start data-subjects facing the decision problem with consent requests from multiple enterprises. Additionally, we demonstrate the scenario of consent recommendation as a prediction problem with minimum attributes available from LinkedIn’s privacy settings.

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