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Impact of Social Learning on Privacy-Preserving Data Collection

Start: 9/24/2018 at 3:00PM
End: 9/24/2018 at 4:00PM
Location: 207 DeBartolo Hall
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Abstract

The explosive growth in data collection is accelerating the use of machine learning in many IoT applications and shaping our networked life. In particular, personal data is often collected based on “informed consent," where users decide whether to report data or not based upon who is collecting the data. This approach is however untenable, because of vague privacy policies and a behind-the-scenes data brokerage market becoming the norm. Indeed, data privacy has become one notorious threat in human civilization, as evidenced by the very recent Facebook scandal. Two fundamental issues remain open: (i) users have no control of data privacy after reporting private data and the use of data is without their knowledge; and (ii) the data collector has sole liability to protect users' private data. 

Making a paradigm shift, we advocate a new approach to privacy-preserving data collection for IoT applications: users control their own data privacy by reporting data with noise injection, and the data collector provides rewards in exchange for receiving more accurate data. We explore a model where users learn (noisy versions of) personal data with social friends. Based on learning from both her personal data and her friends’ noisy data, each user makes strategic decisions to report privacy-preserving data. We developed a Bayesian game theoretic framework to study the impact of social learning on users’ data reporting strategies and devise the payment mechanism for the data collector. Our findings reveal that both the data collector and the users benefit from social learning under some mild conditions.

 

 

 

Dr. Junshan Zhang

Ira A. Fulton Chair Professor

Arizona State University

 

Junshan Zhang received his Ph.D. degree from the School of ECE at Purdue University in 2000. He joined the School of ECEE at Arizona State University in August 2000, where he has been Fulton Chair Professor since 2015. His research interests fall in the general field of information networks and data science, including communication networks, Internet of Things (IoT), Fog Computing, social networks, smart grid. His current research focuses on fundamental problems in information networks and data science, including Fog Computing and its applications in IoT and 5G, IoT data privacy/security, optimization/control of mobile social networks, stochastic modeling and optimization for smart grid.

Professor Zhang is a Fellow of the IEEE, and a recipient of the ONR Young Investigator Award in 2005 and the NSF CAREER award in 2003. He received the IEEE Wireless Communication Technical Committee Recognition Award in 2016. His papers have won a few awards, including the Best Student paper at WiOPT 2018, the Kenneth C. Sevcik Outstanding Student Paper Award of ACM SIGMETRICS/IFIP Performance 2016, the Best Paper Runner-up Award of IEEE INFOCOM 2009 and IEEE INFOCOM 2014, and the Best Paper Award at IEEE ICC 2008 and ICC 2017. Building on his research findings, he co-founded Smartiply Inc in 2015, a Fog Computing startup company delivering boosted network connectivity and embedded artificial intelligence. Prof. Zhang was TPC co-chair for a number of major conferences in communication networks, including IEEE INFOCOM 2012 and ACM MOBIHOC 2015. He was general chair for ACM/IEEE SEC 2017, WiOPT 2016, and IEEE Communication Theory Workshop 2007. He is currently serving as an editor-at-large for IEEE/ACM Transactions on Networking and an editor for IEEE Network Magazine.