Scientific Foundations

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Visible to the public SaTC: STARSS: Metric & CAD for DPA Resistance

Physical side channels pose a big threat to the security of embedded hardware. The differential power analysis (DPA) attack is a well known side channel threat which exploits the linear dependence of the power on the secret data or an intermediate value correlated to the secret data through statistical model building. This project addresses the DPA vulnerability by deploying a technology cell library consisting of private gates. The technique developed will make embedded hardware less vulnerable to side-channel attacks, thereby securing private user data and transactions.

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Visible to the public TWC: Medium: Collaborative: Online Social Network Fraud and Attack Research and Identification

Online social networks (OSNs) face various forms of fraud and attacks, such as spam, denial of service, Sybil attacks, and viral marketing. In order to build trustworthy and secure OSNs, it has become critical to develop techniques to analyze and detect OSN fraud and attacks. Existing OSN security approaches usually target a specific type of OSN fraud or attack and often fall short of detecting more complex attacks such as collusive attacks that involve many fraudulent OSN accounts, or dynamic attacks that encompass multiple attack phases over time.

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Visible to the public EAGER: Guaranteed-Secure and Searchable Genomic Data Repositories

Publicly available and searchable genomic data banks could revolutionize clinical and research settings, but privacy concerns about releasing such information are currently preventing its usage. This project aims to address these concerns by providing new mechanisms by which individuals can donate their genomic information to a data bank in such a way that third parties, such as doctors or researchers, querying the data bank are guaranteed to learn only aggregate functions of the population's data that the individuals authorize.

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Visible to the public CAREER: UCPriv: User-Centric Privacy Management

To date, the application of quantitative security and privacy metrics metrics has seen its greatest successes when exploring the worst-case properties of a system. That is, given a powerful adversary, to what extent does the system preserve some relevant set of properties? While such analyses allow experts to build systems that are resistant to strong attackers, many deployed systems were not designed in this manner. In fact, there is growing evidence that users' privacy is routinely compromised as a byproduct of using social, participatory, and distributed applications.

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Visible to the public TWC: TTP Option: Small: Collaborative: Enhancing Anonymity Network Resilience against Pervasive Internet Attacks

Large-scale Internet censorship prevents citizens of many parts of the world from accessing vast amounts of otherwise publicly available information. The recognition and publication of these censorship events have aided in motivating the development of new privacy-enhancing technologies to circumvent the censor. We argue that as circumvention technologies improve and the cost of detecting their use increases, adversaries that are intent on restricting access to information will seek out alternative techniques for disruption.

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Visible to the public TTP: Small: Collaborative: Defending Against Website Fingerprinting in Tor

The more people use the Internet, the more they risk sharing information they don't want other people to know. Tor is a technology that every day helps millions of people protect their privacy online. Tor users -- ranging from ordinary citizens to companies with valuable intellectual property -- gain protection for the content of their online messages and activities, as well as whom they interact with and when. For the most part, Tor is very secure. However, it has a known vulnerability to an attack called website fingerprinting.

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Visible to the public TWC: Option: Medium: Collaborative: Semantic Security Monitoring for Industrial Control Systems

Industrial control systems differ significantly from standard, general-purpose computing environments, and they face quite different security challenges. With physical "air gaps" now the exception, our critical infrastructure has become vulnerable to a broad range of potential attackers. In this project we develop novel network monitoring approaches that can detect sophisticated semantic attacks: malicious actions that drive a process into an unsafe state without however exhibiting any obvious protocol-level red flags.

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Visible to the public TWC: Medium: Collaborative: Privacy-Preserving Distributed Storage and Computation

This project aims at developing efficient methods for protecting the privacy of computations on outsourced data in distributed settings. The project addresses the design of an outsourced storage framework where the access pattern observed by the storage server gives no information about the actual data accessed by the client and cannot be correlated with external events. For example, the server cannot determine whether a certain item was previously accessed by the client or whether a certain algorithm is being executed.

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Visible to the public EAGER: Collaborative: Algorithmic Framework for Anomaly Detection in Interdependent Networks

Modern critical infrastructure relies on successful interdependent function among many different types of networks. For example, the Internet depends on access to the power grid, which in turn depends on the power-grid communication network and the energy production network. For this reason, network science researchers have begun examining the robustness of critical infrastructure as a network of networks, or a multilayer network. Research in network anomaly detection systems has focused on single network structures (specifically, the Internet as a single network).

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Visible to the public STARSS: Small: Collaborative: Specification and Verification for Secure Hardware

There is a growing need for techniques to detect security vulnerabilities in hardware and at the hardware-software interface. Such vulnerabilities arise from the use of untrusted supply chains for processors and system-on-chip components and from the scope for malicious agents to subvert a system by exploiting hardware defects arising from design errors, incomplete specifications, or maliciously inserted blocks.