Foster Multidisciplinary Approach
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Submitted by Allison Lewko on Mon, 12/18/2017 - 1:32pm
Modern cryptography offers an impressive virtual buffet to a consumer who is wealthy in resources, with powerful tools like fully homomorphic encryption (which allows a provider to compute with encrypted values while keeping the client's data safe) and general purpose obfuscation (which allows one to hide the purpose of a given computation). But for more modestly minded users, who seek to perform less lofty tasks using more affordable computing resources or under more time-tested assumptions, the offerings are comparatively paltry.
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Submitted by Apu Kapadia on Thu, 12/14/2017 - 8:08pm
Social networking and sensor-rich devices such as smartphones are becoming increasingly pervasive in today's society. People can share information concerning their location, activity, fitness, and health with their friends and family while benefiting from applications that leverage such information. Yet, users already find managing their privacy to be challenging, and the complexity involved in doing so is bound to increase.
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Submitted by Salil Vadhan on Thu, 12/14/2017 - 6:41pm
Information technology, advances in statistical computing, and the deluge of data available through the Internet are transforming computational social science. However, a major challenge is maintaining the privacy of human subjects. This project is a broad, multidisciplinary effort to help enable the collection, analysis, and sharing of sensitive data while providing privacy for individual subjects.
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Submitted by David Evans on Mon, 12/11/2017 - 3:30pm
Many compelling applications involve computations that require sensitive data from two or more individuals. For example, as the cost of personal genome sequencing rapidly plummets many genetics applications will soon be within reach of individuals such as comparing one?s genome with the genomes of different groups of participants in a study to determine which treatment is likely to be most effective. Such comparisons could have tremendous value, but are currently infeasible because of the privacy concerns both for the individual and study participants.
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Submitted by Murat Kantarcioglu on Mon, 12/11/2017 - 3:20pm
Data provenance refers to the history of the contents of an object and its successive transformations. Knowledge of data provenance is beneficial to many ends, such as enhancing data trustworthiness, facilitating accountability, verifying compliance, aiding forensics, and enabling more effective access and usage controls. Provenance data minimally needs integrity assurance to realize these benefits.
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Submitted by Reza Curtmola on Mon, 12/11/2017 - 1:15pm
When data is outsourced at a cloud storage provider, data owners lose control over the integrity of their data and must trust the storage provider unconditionally. Coupled with numerous data loss incidents, this prevents organizations from assessing the risk posed by outsourcing data to untrusted clouds, making cloud storage unsuitable for applications that require long-term security and reliability guarantees. This project establishes a practical remote data checking (RDC) framework as a mechanism to provide long-term integrity and reliability for remotely stored data.
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Submitted by John Abowd on Fri, 12/08/2017 - 3:03pm
Safely managing the release of data containing confidential information about individuals is a problem of great societal importance. Governments, institutions, and researchers collect data whose release can have enormous benefits to society by influencing public policy or advancing scientific knowledge. But dissemination of these data can only happen if the privacy of the respondents' data is preserved or if the amount of disclosure is limited.
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Submitted by Shyam Sundar on Tue, 12/05/2017 - 9:46pm
In individual pursuits of personalized service and other functionalities, people disclose personal and private information by trusting certain online sites and services. Scholars often assume that such trust is based on a careful assessment of the benefits and risks of disclosing information online. This project departs from such an assumption and investigates the possibility that decision-making about online information disclosure is not systematic, but rather based on cognitive heuristics (or mental shortcuts) triggered by cues in the interaction context.
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Submitted by Polo Chau on Mon, 12/04/2017 - 7:30pm
Used by hundreds of millions of people every day, online services are central to everyday life. Their popularity and impact make them targets of public opinion skewing attacks, in which those with malicious intent manipulate the image of businesses, mobile applications and products. Website owners often turn to crowdsourcing sites to hire an army of professional fraudsters to paint a fake flattering image for mediocre subjects or trick people into downloading malicious software.
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Submitted by Arvind Narayanan on Mon, 12/04/2017 - 7:21pm
The project develops new technologies for continual, web-scale measurement and rapid defenses against emerging threats to web privacy and security arising from third-party tracking. It draws from the fields of web security, systems, measurement, statistics, and machine learning. The outputs of this project will enable website administrators to find and fix a large class of privacy and security problems. They will help improve existing browser privacy tools.
