Nina Taft is a Senior Staff Research Scientist at Google where she leads the Applied Privacy Research group. For many years, Nina worked in the field of networking, focused on traffic modeling and analysis, protocols, and intrusion detection. She is well known for her work in statistical modeling of city-to-city internet traffic patterns and was chosen as one of the “top-10 women in networking in 2017” (ACM’s N2Women award). Her current interests lie in applications of machine learning for privacy, private data analytics, and user experience. Nina received her Ph.D. from UC Berkeley, and has worked in industrial research labs since then - at SRI, Sprint Labs, Intel Berkeley Labs, Technicolor Research, and now at Google. She is a member of the Scientific Advisory Board for the Max Planck Institute in Germany, has been PC chair of ACM SIGCOMM and IMC, and serves on many program committees for academic conferences.
Abstract : Smartphone app developers face numerous challenges in improving their privacy posture that stem from an evolving world in terms of laws, policy, and users’ privacy preferences. In this talk, we summarize some of the latest approaches and technical advances to help Smartphone app developers improve their privacy posture. We start by summarizing some of the challenges developers face, how their users make decisions about privacy choices, and how users may influence developer design. We discuss examples of nudges that can incentivize developers to be more privacy-friendly. Considerable progress has been achieved recently in using Natural Language Processing techniques to automatically analyze privacy policies, and this lays the groundwork for useful tools. We show how this can assist developers by providing automated feedback, especially on critical issues such as how well an apps’ behavior adheres to its privacy policy.
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