We live in a world in which people are socially connected through an increasing number of mobile devices Information is collected from these devices and exchanged with each other In this talk I will discuss new challenges of data privacy with social connectivity First I will talk about the problem of network alignment, in which we match vertices of two graphs using only network connectivity A successful solution to this problem can be used to launch an attack to reveal identifies of users in one social network (e g , Facebook) if the same group of users also use another platform with identifies shown in public (e g , LinkedIn) We use discrete graph curvature to solve the network alignment problem In the second part I will discuss the setting of mobile agents with their occurrences recorded by mobile devices Such recorded occurrences can be used to infer agent location if some of the agents are not privacy-aware and post their locations occurrences occasionally I will report algorithms of location attacks and show how effective this can be to infer user locations

Jie Gao is currently an Associate Professor at Computer Science department, Stony Brook University. She received Bachelor's degree from the Special Class for the Gifted Young from University of Science and Technology of China in 1999 and Ph.D in Computer Science from Stanford University in 2004. She received the NSF career award in 2006, IMC best paper award and multiple Research Excellence Award in computer science department of Stony Brook. She is currently serving on the editorial board of ACM Transactions on Sensor Networks and Journal of Discrete Algorithms. She published over 120 referred papers in computer networking and theoretical computer science.