This synthesis lecture provides a survey of work on privacy in online social networks (OSNs). This work encompasses concerns of users as well as service providers and third parties. Our goal is to approach such concerns from a computer-science perspective, and building upon existing work on privacy, security, statistical modeling and databases to provide an overview of the technical and algorithmic issues related to privacy in OSNs. We start our survey by introducing a simple OSN data model and describe common statistical-inference techniques that can be used to infer potentially sensitive information. Next, we describe some privacy definitions and privacy mechanisms for data publishing. Finally, we describe a set of recent techniques for modeling, evaluating, and managing individual users' privacy risk within the context of OSNs. Table of Contents: Introduction / A Model for Online Social Networks / Types of Privacy Disclosure / Statistical Methods for Inferring Information in Networks / Anonymity and Differential Privacy / Attacks and Privacy-preserving Mechanisms / Models of Information Sharing / Users' Privacy Risk / Management of Privacy Settings

Autorentext

Elena Zheleva is a Data Scientist at LivingSocial. She received a Ph.D. in Computer Science from the University of Maryland, College Park in 2011. Her research interests lie in data mining and machine learning for social networks and social media, focusing on statistical models for prediction, evolution, and privacy. She has served on the Program Committees for KDD, AAAI, and CIKM. Evimaria Terzi is an Assistant Professor in the Department of Computer Science at Boston University. She received a Ph.D. in Computer Science from the University of Helsinki in 2007 and an M.S. from Purdue University in 2002. Before joining Boston University in 2009, she was a Research Scientist at IBM Research. Her work focuses on algorithmic data mining, with emphasis on time-series and social-network analysis. Evimaria has received the Microsoft Faculty Fellowship, and has been in the PC and Senior PC of many data-mining and database conferences including KDD, VLDB, and SIGMOD. Lise Getoor is an AssociateProfessor in the Computer Science Department at the University of Maryland, College Park. She received her Ph.D. from Stanford University in 2001. Her research interests include machine learning and reasoning under uncertainty; in addition she works in data management, visual analytics, and social network analysis. She is a board member of the International Machine Learning Society, and co-chaired ICML 2011. She has served as associate editor for ACM Transactions of Knowledge Discovery from Data, the Machine Learning Journal, and JAIR, on the AAAI Executive Council, and on the PC or senior PC of conferences including AAAI, ICML, KDD, SIGMOD, UAI, VLDB, and WWW.