Grant Year: 2017

January 14, 2020

Tools and Methods for Inferring Demographic Bias in Social Media datasets

Social media posts from smartphones are an increasingly useful data source for researchers and policymakers. For example, place-based posts can help city planners assess how infrastructure or public space is being used, and help identify the needs of different communities. But it is important to know who is represented in…

January 14, 2020

Programming for Privacy in High School Classrooms

Learning about online privacy early (beginning in high school) can be an effective entree to complex topics in cybersecurity and networking, yet many teachers have expressed concern that such non-programming content might not be compatible with their schools’ computer-science standards. A team of researchers will develop “Teaching Privacy” lessons that…

January 14, 2020

Secure & Usable Backup Authentication

Backup authentication is a crucial yet often overlooked problem in cybersecurity. Passwords and other methods of authentication are fixtures of digital life, but the processes by which we recover our passwords and other authentication methods are less well understood or studied. This research will focus on making backup authentication more…

January 14, 2020

Addressing the Privacy Gaps in Healthcare

The projected cost of cyber attacks on U.S. healthcare systems is over $305 billion over the next five years. To address this threat, this team will study a mathematical model of privacy that incorporates the factors related to privacy preferences that have been studied in social sciences. They will validate…

January 14, 2020

Securing Protected Health Information in Mobile Health Devices

These researchers will address cybersecurity problems related to protected health information (PHI) on mobile health devices. Drawing upon theory from statistics, machine learning, principal-agent modeling, and mean-field game models, they will design scalable machine-learning methods to identify duplicate or stolen/counterfeit PHI in order to secure the use of PHI for…

January 14, 2020

Adversarially Robust Machine Learning

Machine learning provides valuable methodologies for detecting and protecting against security attacks at scale. However, a machine-learning algorithm used for security is different from other domains because in a security setting, an adversary will try to adapt his behavior to avoid detection. This research team will explore methodologies for improving…

January 14, 2020

Allegro: A Framework for Practical Differential Privacy of SQL Queries

Current approaches for data security and privacy fail to reconcile the seemingly contradictory goals of leveraging data for positive outcomes while guaranteeing privacy protection for individuals. One promising approach is differential privacy, which allows general statistical analysis of data while providing individuals with a strong formal guarantee of privacy. This…

January 14, 2020

Analysis of Security Breaches of Local Law Enforcement Agency Data

This research project will conduct a preliminary assessment of whether security breaches of local law enforcement agency data are sufficiently numerous and serious as to be worthy of public concern and a policy response. If warranted, it will also set out tentative recommendations for policy changes. The study will entail…

January 14, 2020

Citizen Advocacy in a Connected World

As connected city initiatives become increasingly common, the potential benefits, such as increased efficiency and improved security, come with significant potential harms, including privacy and security vulnerabilities, as well as the danger of exacerbating socioeconomic disparity. To bring awareness to these issues and encourage community-based discussion, the team plans to…

January 14, 2020

Computing on Encrypted Databases with No Information Leakage

Encrypted databases enable users to compute queries while the data remains encrypted, but encryption alone does not suffice to protect sensitive information because queries leak sensitive information through side channels. For example, factors such as the size of the output or the timing of a query may be leaked. This…