Trusted CI Webinar: Enforcing Security and Privacy Policies to Protect Research Data

Tian, Yuan

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https://hdl.handle.net/2142/108880 Copy

Description

Title

Trusted CI Webinar: Enforcing Security and Privacy Policies to Protect Research Data

Author(s)

Tian, Yuan

Issue Date

2020-10-26

Keyword(s)

• Trusted CI
• Webinar
• NSF
• Cybersecurity
• Security Policy
• Privacy
• IoT
• Mobile Apps

Abstract

Advances in computer systems over the past decade have laid a solid foundation for data collection at a staggering scale. Data generated from end-user devices has tremendous value to the research community. For example, mobile and Internet-of-Things devices can participate in large-scale Internet-based measurement or monitoring of patient's health conditions. While ground-breaking discovered may occur, malicious attacks or unintentional data leaks threaten the research data. Such a threat is hard to predict and difficult to recover from once it happens. Preventative and defensive measures should be taken where data is generated in order to protect private, valuable data from the attackers. Currently, there are efforts that try to regulate data management, for example, a research application might have a privacy policy that describes how the user data is being collected and protected. However, there is a disconnect between these documented policies and the implementations of a research project. In this talk, I’ll present our research, which interprets the documented policies automatically with NLP (natural language processing) and enforce them in the code of research projects, in order to protect the privacy of research data. This work can significantly reduce researchers' overhead in implementing policy-compliant code and reduce the complexity of protecting research datasets. Speaker Bio: Yuan Tian is an Assistant Professor of Computer Science at University of Virginia. Her research focuses on security and privacy and its interactions with systems, and machine learning. Her work has a real-world impact on platforms (such as iOS, Chrome, and Azure). She is a recipient of NSF CAREER Award 2020, Amazon Faculty Fellowship 2019, CSAW Best Paper Award 2019, Rising Stars in EECS 2016.

Type of Resource

• text
• still image

Language

en

Permalink

http://hdl.handle.net/2142/108880

Sponsor(s)/Grant Number(s)

• NSF Grant # 1920430
• NSF Grant # 1920462

Copyright and License Information

Copyright © 2020 The Trustees of Indiana University. This work is licensed under a Creative Commons Attribution-­NonCommercial 3.0 Unported (CC BY­NC 3.0) license.

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