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Security and Privacy in Data Science

March 30 (2022) @ 3:00 pm - 4:00 pm

Data science is the process from collection of data to the use of new insights gained from this data. It is at the core of the big data and machine learning revolution fueling the digitization of our economy. The integration of data science and machine learning into digital and cyber-physical processes and the often sensitive nature of personally identifiable data used in the process, expose the data science process to security and privacy threats. In this talk I will review three exemplary security and privacy problems in different phases of the data science lifecycle and show potential countermeasures. First, I will show how to enhance the privacy of data collection using secure multi-party computation and differential privacy. Second, I will show how to protect data outsourced to a cloud database system and still perform efficient queries using keyword PIR and homomorphic encryption. Last, I will show that differential privacy does not protect against membership inference attacks as expected.

 

Zoom meeting link: https://newcastleuniversity.zoom.us/j/85034819175?pwd=bVZtNjdTRXFqR3VGMDUyRGVJUlB1UT09

Meeting ID: 850 3481 9175
Passcode: 854204

Youtube Live Streaming: https://youtu.be/OcRm8JMztDc

Details

Date:
March 30 (2022)
Time:
3:00 pm - 4:00 pm
Seminar Tags:
,

Presenter

Florian Kerschbaum (University of Waterloo)

Florian Kerschbaum is an associate professor in the David R. Cheriton School of Computer Science at the University of Waterloo (since 2017), a member of the CrySP group, and NSERC/RBC chair in data security (since 2019). Before he worked as chief research expert at SAP in Karlsruhe (2005 – 2016) and as a software architect at Arxan Technologies in San Francisco (2002 – 2004). He holds a Ph.D. in computer science from the Karlsruhe Institute of Technology (2010) and a master's degree from Purdue University (2001). He served as the inaugural director of the Waterloo Cybersecurity and Privacy Institute (2018 – 2021). He is an ACM Distinguished Scientist (2019) and a winner of the Outstanding Young Computer Science Researcher Award from CS-Can/Info-Can (2019). He is interested in security and privacy in the entire data science lifecycle. He extends real-world systems with cryptographic security mechanisms to achieve (some) provable security guarantees. His work is used in several business applications.

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