Foster Multidisciplinary Approach
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Submitted by Giuseppe Ateniese on Tue, 12/19/2017 - 10:20am
Informally speaking, Secure Multi-Party Computation (SMPC) allows two or more parties to jointly compute some function on their private inputs in a distributed fashion (i.e., without the involvement of a trusted third party) such that none of the parties learns anything beyond its dedicated output and what it can deduce from considering both this output and its own private input. Since its inception in 1982 by Yao, SMPC has advanced greatly and over the years a large body of work has been developed.
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Submitted by Yang Wang on Mon, 12/18/2017 - 5:52pm
Technology advances have brought numerous benefits to people and society, but also heightened risks to privacy. This project will investigate mechanisms and build tools to help people make privacy-aware decisions in different online contexts. The outcomes will help people to better understand their own privacy preferences and behavior, and enable them to better manage their privacy on the Internet. The project will create designs that can be integrated into mobile app markets and web browsers. The results will also inform Internet standards and governmental policies on Internet privacy.
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Submitted by Abhishek Gupta on Mon, 12/18/2017 - 5:18pm
Cyber-physical systems have increasingly become top targets for hackers around the world. We are also seeing proliferation of internet-connected critical infrastructures that allow for easy monitoring, visualization, and control. In February 2013, US president signed an executive order "Improving Critical Infrastructure Cybersecurity" that underscores the urgent need for securing such critical infrastructure against malicious attacks.
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Submitted by Farinaz Koushanfar on Mon, 12/18/2017 - 3:44pm
The growing hardware security community is faced with an immediate need to develop effective tools and benchmarks. The purpose of this project is to lead a community-wide movement toward stronger assurances in our integrated circuits, computational platforms, and electronics supply chain.
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Submitted by Kui Ren on Mon, 12/18/2017 - 3:39pm
The economics of Cloud Computing Cloud Computing impels a fundamental shift in how data services are deployed and delivered, enabling flexible, dynamic outsourcing while reducing capital cost commitments for hardware and software. However, cloud computing also deprives customers of direct control over the systems that manage their data, raising security and privacy concerns.
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Submitted by Shuangqing Wei on Mon, 12/18/2017 - 3:35pm
The project aims at quantifying a general network's inner potential for supporting various forms of security by achieving secret common randomness between pairs or groups of its nodes. Statistical and computational secrecy measures are being considered against a general passive adversary. Common-randomness-achieving protocols are classified into two groups: culture-building and crowd-shielding. The former achieves common randomness between nodes situated in close proximity of each other, from correlated observations of specific (natural or induced) network phenomena.
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Submitted by Kamalika Chaudhuri on Mon, 12/18/2017 - 3:24pm
Machine learning on large-scale patient medical records can lead to the discovery of novel population-wide patterns enabling advances in genetics, disease mechanisms, drug discovery, healthcare policy, and public health. However, concerns over patient privacy prevent biomedical researchers from running their algorithms on large volumes of patient data, creating a barrier to important new discoveries through machine-learning. The goal of this project is to address this barrier by developing privacy-preserving tools to query, cluster, classify and analyze medical databases.
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Submitted by lalithasankar on Mon, 12/18/2017 - 3:13pm
Information sharing between operators (agents) in critical infrastructure systems such as the Smart Grid is fundamental to reliable and sustained operation. The contention, however, between sharing data for system stability and reliability (utility) and withholding data for competitive advantage (privacy) has stymied data sharing in such systems, sometimes with catastrophic consequences. This motivates a data sharing framework that addresses the competitive interests and information leakage concerns of agents and enables timely and controlled information exchange.
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Submitted by Rachel Greenstadt on Mon, 12/18/2017 - 3:06pm
Increasing amounts of data are being collected about users, and increasingly sophisticated analytics are being applied to this data for various purposes. Privacy analytics are machine learning and data mining algorithms applied by end-users to their data for the purpose of helping them manage both private information and their self-presentation.
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Submitted by cardenas on Mon, 12/18/2017 - 2:59pm
This project focuses on tackling the security and privacy of Cyber-Physical Systems (CPS) by integrating the theory and best practices from the information security community as well as practical approaches from the control theory community. The first part of the project focuses on security and protection of cyber-physical critical infrastructures such as the power grid, water distribution networks, and transportation networks against computer attacks in order to prevent disruptions that may cause loss of service, infrastructure damage or even loss of life.
