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Privacy in the Modern Era: The Cases of Online Social Network and Machine Learning Model

发布日期:2018年12月19日 10:45 点击次数:

时间 12月27日(周四)14:00-15:00 地点 软件园校区教研楼2楼会议室
本站讯 讲座时间 2018-12-27 14:00:00

一、报告题目

Privacy in the Modern Era: The Cases of Online Social Network and Machine Learning Model

二、报告人

Yang Zhang

二、报告时间

2018年12月27日14:00-15:00

三、报告地点

教研楼2楼会议室

四、报告人简介

Yang Zhang (https://yangzhangalmo.github.io/) is currently a postdoc working in the group of professor Michael Backes at CISPA Helmholtz Center for Information Security, Saarbruecken, Germany. From January 2019, he will be an independent research group leader at CISPA. His research mainly concentrates on privacy in modern society. Topics include machine learning privacy, biomedical privacy, social network privacy, and location privacy. Besides, he also works on urban informatics/computing, social media analysis, and data mining. Yang obtained his Ph.D. degree from University of Luxembourg on 2016.11. Prior to that, he obtained his bachelor (2009) and master (2012) degrees from Shandong University, China.

五、报告摘要

The advancement of information and communication technologies has resulted in a deluge of data being available. While serving as the fuel for the next-generation industrial development, the large-scale data also raises severe privacy concerns. In this talk, I will cover our recent work on assessing privacy risks stemming from social network data and machine learning models. First, I will focus on the phenomenal location check-ins and hashtags shared in online social networks. We show that location check-ins can be used as an effective attack vector to infer users' social relations. Moreover, I'll demonstrate how hashtags can be exploited to effectively pinpoint a user's fine-grained locations. Second, I will discuss the risks of training data privacy in machine learning models. We relax various assumptions of the adversary model and show that membership inference attacks against machine learning classifiers can be performed in a much cheaper and effective way. Our results further shed light on the necessity of a general evaluation methodology for machine learning models in the future. The corresponding research papers of this talk are published at CCS 2017, WWW 2018, and NDSS 2019.


【作者:张穆楠        来自:软件学院    责任编辑:陈泠杰 张丹丹  】

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