The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform

Elaine Shi - One of the best experts on this subject based on the ideXlab platform.

  • memory trace oblivious program execution
    IEEE Computer Security Foundations Symposium, 2013
    Co-Authors: Chang Liu, Michael Hicks, Elaine Shi
    Abstract:

    Cloud computing allows users to delegate data and computation to cloud service providers, at the cost of giving up physical control of their computing infrastructure. An attacker (e.g., insider) with physical access to the computing platform can perform various physical attacks, including probing memory buses and cold-boot style attacks. Previous work on secure (co-)processors provides hardware support for memory encryption and prevents direct leakage of Sensitive data over the memory bus. However, an adversary snooping on the bus can still Infer Sensitive Information from the memory access traces. Existing work on Oblivious RAM (ORAM) provides a solution for users to put all data in an ORAM; and accesses to an ORAM are obfuscated such that no Information leaks through memory access traces. This method, however, incurs significant memory access overhead. This work is the first to leverage programming language techniques to offer efficient memory-trace oblivious program execution, while providing formal security guarantees. We formally define the notion of memory-trace obliviousness, and provide a type system for verifying that a program satisfies this property. We also describe a compiler that transforms a program into a structurally similar one that satisfies memory trace obliviousness. To achieve optimal efficiency, our compiler partitions variables into several small ORAM banks rather than one large one, without risking security. We use several example programs to demonstrate the efficiency gains our compiler achieves in comparison with the naive method of placing all variables in the same ORAM.

Chang Liu - One of the best experts on this subject based on the ideXlab platform.

  • memory trace oblivious program execution
    IEEE Computer Security Foundations Symposium, 2013
    Co-Authors: Chang Liu, Michael Hicks, Elaine Shi
    Abstract:

    Cloud computing allows users to delegate data and computation to cloud service providers, at the cost of giving up physical control of their computing infrastructure. An attacker (e.g., insider) with physical access to the computing platform can perform various physical attacks, including probing memory buses and cold-boot style attacks. Previous work on secure (co-)processors provides hardware support for memory encryption and prevents direct leakage of Sensitive data over the memory bus. However, an adversary snooping on the bus can still Infer Sensitive Information from the memory access traces. Existing work on Oblivious RAM (ORAM) provides a solution for users to put all data in an ORAM; and accesses to an ORAM are obfuscated such that no Information leaks through memory access traces. This method, however, incurs significant memory access overhead. This work is the first to leverage programming language techniques to offer efficient memory-trace oblivious program execution, while providing formal security guarantees. We formally define the notion of memory-trace obliviousness, and provide a type system for verifying that a program satisfies this property. We also describe a compiler that transforms a program into a structurally similar one that satisfies memory trace obliviousness. To achieve optimal efficiency, our compiler partitions variables into several small ORAM banks rather than one large one, without risking security. We use several example programs to demonstrate the efficiency gains our compiler achieves in comparison with the naive method of placing all variables in the same ORAM.

Michael Hicks - One of the best experts on this subject based on the ideXlab platform.

  • memory trace oblivious program execution
    IEEE Computer Security Foundations Symposium, 2013
    Co-Authors: Chang Liu, Michael Hicks, Elaine Shi
    Abstract:

    Cloud computing allows users to delegate data and computation to cloud service providers, at the cost of giving up physical control of their computing infrastructure. An attacker (e.g., insider) with physical access to the computing platform can perform various physical attacks, including probing memory buses and cold-boot style attacks. Previous work on secure (co-)processors provides hardware support for memory encryption and prevents direct leakage of Sensitive data over the memory bus. However, an adversary snooping on the bus can still Infer Sensitive Information from the memory access traces. Existing work on Oblivious RAM (ORAM) provides a solution for users to put all data in an ORAM; and accesses to an ORAM are obfuscated such that no Information leaks through memory access traces. This method, however, incurs significant memory access overhead. This work is the first to leverage programming language techniques to offer efficient memory-trace oblivious program execution, while providing formal security guarantees. We formally define the notion of memory-trace obliviousness, and provide a type system for verifying that a program satisfies this property. We also describe a compiler that transforms a program into a structurally similar one that satisfies memory trace obliviousness. To achieve optimal efficiency, our compiler partitions variables into several small ORAM banks rather than one large one, without risking security. We use several example programs to demonstrate the efficiency gains our compiler achieves in comparison with the naive method of placing all variables in the same ORAM.

Shi Elaine - One of the best experts on this subject based on the ideXlab platform.

  • Memory Trace Oblivious Program Execution
    2013
    Co-Authors: Liu Chang, Hicks Michael, Shi Elaine
    Abstract:

    Cloud computing allows users to delegate data and computation to cloud service providers, at the cost of giving up physical control of their computing infrastructure. An attacker (e.g., insider) with physical access to the computing platform can perform various physical attacks, including probing memory buses and cold-boot style attacks. Previous work on secure (co-)processors provides hardware support for memory encryption and prevents direct leakage of Sensitive data over the memory bus. However, an adversary snooping on the bus can still Infer Sensitive Information from the memory access traces. Existing work on Oblivious RAM (ORAM) provides a solution for users to put all data in an ORAM; and accesses to an ORAM are obfuscated such that no Information leaks through memory access traces. This method, however, incurs significant memory access overhead. In this work, we are among the first to leverage programming language techniques to offer efficient memory-trace oblivious program execution, while providing formal security guarantees. We first formally define the notion of memory-trace obliviousness, and provide a type system for verifying that a program satisfies this property. We then design a compiler that transforms a program into one that satisfies memory trace obliviousness. To achieve optimal efficiency, our compiler aims to minimize the usage of ORAM whenever possible, and would partition variables in smaller ORAM banks (which are faster to access than larger ORAM banks) without risking security. We use several example programs to demonstrate the efficiency gains our compiler achieves in comparison with the naive method of placing all variables in the same ORAM

Wesley W Chu - One of the best experts on this subject based on the ideXlab platform.

  • protection of database security via collaborative Inference detection
    Intelligence and Security Informatics, 2008
    Co-Authors: Yu Chen, Wesley W Chu
    Abstract:

    Malicious users can exploit the correlation among data to Infer Sensitive Information from a series of seemingly innocuous data accesses. Thus, we develop an Inference violation detection system to protect Sensitive data content. Based on data dependency, database schema and semantic knowledge, we constructed a semantic Inference model (SIM) that represents the possible Inference channels from any attribute to the pre-assigned Sensitive attributes. The SIM is then instantiated to a semantic Inference graph (SIG) for query-time Inference violation detection. For a single user case, when a user poses a query, the detection system will examine his/her past query log and calculate the probability of Inferring Sensitive Information. The query request will be denied if the Inference probability exceeds the prespecified threshold. For multi-user cases, the users may share their query answers to increase the Inference probability. Therefore, we develop a model to evaluate collaborative Inference based on the query sequences of collaborators and their task-Sensitive collaboration levels. Experimental studies reveal that Information authoritativeness, communication fidelity and honesty in collaboration are three key factors that affect the level of achievable collaboration. An example is given to illustrate the use of the proposed technique to prevent multiple collaborative users from deriving Sensitive Information via Inference.

  • database security protection via Inference detection
    Lecture Notes in Computer Science, 2006
    Co-Authors: Yu Chen, Wesley W Chu
    Abstract:

    Access control mechanisms are commonly used to provide control over who may access Sensitive Information. However, malicious users can exploit the correlation among the data and Infer Sensitive Information from a series of seemingly innocuous data access. In this paper, we proposed a detection system that utilizes both the user's current query and past query log to determine if the current query answer can Infer Sensitive Information. This detection system is being extended to the cases of multiple collaborative users based on the query history of all the users and their collaborative levels for specific Sensitive Information.