@inproceedings{77fb988d749e4b75969188b4a77bca55,
title = "Towards behavioral privacy: How to understand AI's privacy threats in ubiquitous computing",
abstract = "Human behavior is increasingly sensed and recorded and used to create models that accurately predict the behavior of consumers, employees, and citizens. While behavioral models are important in many domains, the ability to predict individuals' behavior is in the focus of growing privacy concerns. The legal and technological measures for privacy do not adequately recognize and address the ability to infer behavior and traits. In this position paper, we first analyze the shortcoming of existing privacy theories in addressing AI's inferential abilities. We then point to legal and theoretical frameworks that can adequately describe the potential of AI to negatively affect people's privacy. We then present a technical privacy measure that can help bridge the divide between legal and technical thinking with respect to AI and privacy.",
keywords = "Artificial intelligence, Machine learning, Privacy",
author = "Eran Toch and Yoni Birman",
note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 2018 Joint ACM International Conference on Pervasive and Ubiquitous Computing, UbiComp 2018 and 2018 ACM International Symposium on Wearable Computers, ISWC 2018 ; Conference date: 08-10-2018 Through 12-10-2018",
year = "2018",
month = oct,
day = "8",
doi = "10.1145/3267305.3274155",
language = "الإنجليزيّة",
series = "UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers",
pages = "931--936",
booktitle = "UbiComp/ISWC 2018 - Adjunct Proceedings of the 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2018 ACM International Symposium on Wearable Computers",
}