A new paper was recently presented at “CLaw: The Fourth International Workshop on Legal and Technical Issues in Cloud and Pervasive Computing”, held in Singapore.
The short paper, written by Eran Toch and Yoni Birman, aims to provide a motivation and framework to behavioral privacy: a measure of the ability of adversaries to predict our future behavior.
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.