Home > Seminars > Ling Liu - Trust and Privacy of Deep Learning in Adversarial Settings

Ling Liu - Trust and Privacy of Deep Learning in Adversarial Settings

Start:

3/21/2019 at 3:30PM

End:

3/21/2019 at 4:45PM

Location:

126 DeBartolo

Host:

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Dong Wang

Dong Wang

VIEW FULL PROFILE Email: dwang5@nd.edu
Phone: 574-631-3749
Website: http://www.nd.edu/~dwang5
Office: 214B Cushing

Affiliations

College of Engineering Assistant Professor
Big Data Analytics, Cyber-Physical Systems (CPS), Social Sensing, Smart Cities, Internet of Things (IoT), Network Science
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574-631-3749
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We are entering an exciting era where human intelligence is being enhanced by machine intelligence through big data fueled artificial intelligence (AI) and machine learning (ML). However, recent work shows that prediction models trained privately are vulnerable to adversarial examples and privacy invasion, both of which turn AI and ML against itself through inference attacks and both maliciously manipulate the prediction outputs with only a black box access to a machine learning as a service API. We argue that the trustworthiness should be an essential and mandatory component of a deep learning system for algorithmic decision making. This includes (1) the understanding and the measurement of the level of trust and/or distrust that we place on a deep learning algorithm to perform reliably and truthfully, and (2) the development of formal metrics to quantitatively evaluate and measure the trust level of an algorithmic decision making result by examining the trustworthiness of the algorithm with respect of intentional and unintentional effects of execution, in the presence of different adversarial settings. In this talk, I will share some of our empirical results and characterization on trust and privacy of deep learning in adversarial settings.

Seminar Speaker:

Ling Liu

Ling Liu

Georgia Tech

Prof. Dr. Ling Liu is a Professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL), examining various aspects of large-scale data intensive systems. Prof. Liu is an internationally recognized expert in the areas of Big Data Systems and Analytics, Distributed Systems, Database and Storage Systems, Internet Computing, Privacy, Security and Trust. Prof. Liu has published over 300 international journal and conference articles, and is a recipient of the best paper award from a number of top venues, including ICDCS 2003, WWW 2004, 2005 Pat Goldberg Memorial Best Paper Award, IEEE CLOUD 2012, IEEE ICWS 2013, ACM/IEEE CCGrid 2015, IEEE Edge 2017. Prof. Liu is an elected IEEE Fellow and a recipient of IEEE Computer Society Technical Achievement Award. Prof. Liu has served as general chair and PC chairs of numerous IEEE and ACM conferences in the fields of big data, cloud computing, data engineering, distributed computing, very large databases, World Wide Web, and served as the editor in chief of IEEE Transactions on Services Computing from 2013-2016. Currently Prof. Liu is co-PC chair of The Web 2019 (WWW 2019) and the Editor in Chief of ACM Transactions on Internet Technology (TOIT). Prof. Liu’s research is primarily sponsored by NSF, IBM and Intel.