{Baden09} Randolph Baden, Adam Bender, Daniel Starin, Neil Spring, Bobby Bhattacharjee. Persona: An Online Social Network with User-Defined Privacy. In Proc. of ACM SIGCOMM, 2009.
{Puttaswamy09} Krishna P. N. Puttaswamy, Alessandra Sala, and Ben Y. Zhao. StarClique: Guaranteeing User Privacy in Social Networks Against Intersection Attacks. In Proc. of ACM Conference on emerging Networking EXperiments and Technologies (CoNEXT), 2009.
{Liu11a} Dongtao Liu, Amre Shakimov, Ramón Cáceres, Alexander Varshavsky, and Landon P. Cox. Confidant: Protecting OSN Data without Locking it Up. Proc. of The ACM/IFIP/USENIX 12th International Middleware Conference (Middleware'11), Lisboa, Portugal, December 2011.
{Liu11b} Yabing Liu, Krishna P. Gummadi, Balachander Krishnamurthy, and Alan Mislove. Analyzing Facebook Privacy Settings: User Expectations vs. Reality. Proc. of ACM IMC, 2011. privacy settings match users' expectations only 37% of the time, and when incorrect, almost always expose content to more users than expected
{Akcora12} Cuneyt Gurcan Akcora, Barbara Carminati, Elena Ferrari. Privacy in Social Networks: How Risky is Your Social Graph? Proc. of IEEE ICDE, 2012.
{Bilogrevic13} Igor Bilogrevic, Kévin Huguenin, Berker Agir, Murtuza Jadliwala, Jean-Pierre Hubaux. Adaptive information-sharing for privacy-aware mobile social networks. Proc. of ACM UbiComp, 2013.
{Polakis13} Iasonas Polakis, Stamatis Volanis, Elias Athanasopoulos, Evangelos P. Markatos. The Man Who Was There: Validating Check-ins in Location-based Services. Proc. of ACM ACSAC, 2013.
{Peddinti14} S.T. Peddinti, K.W. Ross, J. Cappos, On the Internet, nobody knows you’re a dog": A Twitter Case Study of Anonymity in Social Networks. Proc. of ACM COSN, 2014. the prevalence and behavior of Anonymous and Identifiable users | AMT | Anonymous users are generally less inhibited to be active participants
{Minkus14} T. Minkus and K.W. Ross, I Know What You're Buying: Privacy Breaches on eBay, Privacy Enhancing Technologies (PETS), 2014. recover purchase histories
{Minkus15} T. Minkus, K. Liu, and K.W. Ross, "Children Seen But Not Heard: When Parents Compromise Children's Online Privacy. Proc. of WWW, 2015. privacy leaking for children; Face++ based age estimation; survey hosted on the Qualtrics platform
{Rossi15} L. Rossi, M. J. Williams, C. Stich and M. Musolesi. Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks. Proc. of AAAI ICWSM, 2015. different types of venues display different discriminative power in terms of user identity
{Bilogrevic15} I. Bilogrevic, K. Huguenin, S. Mihaila, R. Shokri and J.-P. Hubaux, Predicting Users' Motivations behind Location Check-Ins and Utility Implications of Privacy Protection Mechanisms, Network and Distributed System Security (NDSS), 2015. privacy and utility
{Polakis15} Iasonas Polakis, George Argyros, Theofilos Petsios, Suphannee Sivakorn, and Angelos D. Keromytis. Where's Wally?: Precise User Discovery Attacks in Location Proximity Services. Proc. of ACM CCS, 2015. User location discovery
{Ilia15} Panagiotis Ilia, Iasonas Polakis, Elias Athanasopoulos, Federico Maggi, and Sotiris Ioannidis. 2015. Face/Off: Preventing Privacy Leakage From Photos in Social Networks. Proc. of ACM CCS, 2015.
{Yang16} Dingqi Yang, Daqing Zhang, Bingqing Qu, and Philippe Cudré-Mauroux. PrivCheck: privacy-preserving check-in data publishing for personalized location based services. Proc. of ACM UbiComp, 2016.
{Cobb17} Camille Cobb, Tadayoshi Kohno. How Public Is My Private Life? Privacy in Online Dating. Proc. of WWW, 2017. tensions between privacy and competing user values and goals
{Backes17} Michael Backes, Mathias Humbert, Jun Pang, Yang Zhang. walk2friends: Inferring Social Links from Mobility Profiles. Proc. of ACM CCS, 2017. bipartite graph embedding
{Yu18} Jun Yu, Zhenzhong Kuang, Baopeng Zhang, Wei Zhang, Dan Lin, and Jianping Fan. Leveraging Content Sensitiveness and User Trustworthiness to Recommend Fine-Grained Privacy Settings for Social Image Sharing. IEEE Transactions on Information Forensics and Security, 2018, 13(5):1317-1332. a new approach to recommend fine-grained privacy settings for social image sharing, where both the image content sensitiveness and the user trustworthiness are simultaneously considered and integrated to train more discriminative tree classifier
{Wu21} Zongda Wu, Guiling Li, Shigen Shen, Xinze Lian, Enhong Chen & Guandong Xu. Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web, 2021, 24:25–49.
{Gao22} Tianchong Gao, Feng Li. Machine Learning-based Online Social Network Privacy Preservation. Proc. of ACM ASIA CCS, 2022.