CAREER

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Visible to the public CAREER: A Dual-VM Binary Code Reuse Based Framework for Automated Virtual Machine Introspection

Virtual Machine Monitors (VMMs) and hypervisors have become a foundational technology for system developers to achieve increased levels of security, reliability, and manageability for large-scale computing systems such as cloud computing. However, when developing software at the VMM layer, developers often need to interpret the very low level hardware layer state and reconstruct the semantic meanings of the guest operating system events due to the lack of operating system level abstractions.

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Visible to the public CAREER: Secure and Trustworthy Provenance for Accountable Clouds

Cloud computing has emerged as one of the most successful computing models in recent years. However, lack of accountability and non-compliance with data protection regulations have prevented major users such as business, healthcare, and defense organizations from utilizing clouds for sensitive data and applications. Due to the lack of information about cloud internals and the inability to perform trustworthy audits, today's clouds are often not used in regulated industries, preventing their widespread adoption.

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Visible to the public CAREER: Practical Leakage Resilience: Provable Side-Channel Resistance for Embedded Systems

The security of pervasive computing devices relies on cryptographic engines which are usually considered the most trusted part of the system. An immanent threat to embedded cryptographic engines are physical attacks. Practical countermeasures against physical attacks are not completely fail-safe and overly expensive for most applications. Theoretical approaches, however, still tend to have imperfect leakage models and wrong or impractical assumptions about the abilities of cryptographic sub-primitives.

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Visible to the public CAREER: An Axiomatic Basis for Statistical Privacy

Statistical privacy is the art of releasing the datasets that provide useful information about population trends without revealing private information about any individual. Recent high-profile attacks on datasets released by AOL and Netflix demonstrate the need for rigorous application-specific privacy definitions to guide the anonymization of data. The goal of this project is to develop modular components, called privacy axioms, that can be chained together to create customized privacy definitions and anonymized data for statistical privacy applications.

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Visible to the public CAREER: Evidence in Federated Distributed Systems

There is an increasing trend towards federated distributed systems, i.e., systems that are operated jointly by multiple different organizations or individuals. The interests of the participants in such a system are often highly diverse and/or in conflict with one another; for example, participants may be business competitors or based in hostile nations. Thus, federated systems are inherently vulnerable to insider attacks: the participants can try to subvert the system, exploit it for their own benefit, or attack other participants.

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Visible to the public CAREER: Secure and Privacy-assured Data Service Outsourcing in Cloud Computing

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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Visible to the public CAREER: Differentially-Private Machine Learning with Applications to Biomedical Informatics

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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Visible to the public CAREER: RUI: Understanding Human Cognition in Computer Network Defense

The cyber security threat to organizations and governments has continued to grow with increasing dependence on information technology; meanwhile, the entities behind cyber attacks increase in sophistication. Cyber security professionals, the individuals responsible for keeping organizations secure, investigate network activity to find, identify, and respond to threats. These individuals are among the last lines of defense for an organization. Cyber security professionals depend on automated tools to perform their jobs but must make critical decisions that impact security.

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Visible to the public CAREER: Privacy-preserving learning for distributed data

Medical technologies such as imaging and sequencing make it possible to gather massive amounts of information at increasingly lower cost. Sharing data from studies can advance scientific understanding and improve healthcare outcomes. Concern about patient privacy, however, can preclude open data sharing, thus hampering progress in understanding stigmatized conditions such as mental health disorders.

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Visible to the public CAREER: Privacy-Guaranteed Distributed Interactions in Critical Infrastructure Networks

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.