The Experts below are selected from a list of 108 Experts worldwide ranked by ideXlab platform

Guy N. Rothblum - One of the best experts on this subject based on the ideXlab platform.

  • FOCS - A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
    2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010
    Co-Authors: Moritz Hardt, Guy N. Rothblum
    Abstract:

    We consider statistical Data analysis in the interactive setting. In this setting a trusted curator maintains a Database of sensitive information about individual participants, and releases privacy-preserving answers to queries as they arrive. Our primary contribution is a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) queries that arrive online and may be adaptively chosen. This is the first mechanism with worst-case accuracy guarantees that can answer large numbers of interactive queries and is {\em efficient} (in terms of the runtime's dependence on the Data universe size). The error is asymptotically \emph{optimal} in its dependence on the number of participants, and depends only logarithmically on the number of queries being answered. The running time is nearly {\em linear} in the size of the Data universe. As a further contribution, when we relax the utility requirement and require accuracy only for Databases drawn from a rich class of Databases, we obtain exponential improvements in running time. Even in this relaxed setting we continue to guarantee privacy for {\em any} input Database. Only the utility requirement is relaxed. Specifically, we show that when the input Database is drawn from a {\em smooth} distribution — a distribution that does not place too much weight on any Single Data Item — accuracy remains as above, and the running time becomes {\em poly-logarithmic} in the Data universe size. The main technical contributions are the application of multiplicative weights techniques to the differential privacy setting, a new privacy analysis for the interactive setting, and a technique for reducing Data dimensionality for Databases drawn from smooth distributions.

  • A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
    2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010
    Co-Authors: Moritz Hardt, Guy N. Rothblum
    Abstract:

    We consider statistical Data analysis in the interactive setting. In this setting a trusted curator maintains a Database of sensitive information about individual participants, and releases privacy-preserving answers to queries as they arrive. Our primary contribution is a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) queries that arrive online and may be adaptively chosen. This is the first mechanism with worst-case accuracy guarantees that can answer large numbers of interactive queries and is efficient (in terms of the runtime's dependence on the Data universe size). The error is asymptotically optimal in its dependence on the number of participants, and depends only logarithmically on the number of queries being answered. The running time is nearly linear in the size of the Data universe. As a further contribution, when we relax the utility requirement and require accuracy only for Databases drawn from a rich class of Databases, we obtain exponential improvements in running time. Even in this relaxed setting we continue to guarantee privacy for any input Database. Only the utility requirement is relaxed. Specifically, we show that when the input Database is drawn from a smooth distribution - a distribution that does not place too much weight on any Single Data Item - accuracy remains as above, and the running time becomes poly-logarithmic in the Data universe size. The main technical contributions are the application of multiplicative weights techniques to the differential privacy setting, a new privacy analysis for the interactive setting, and a technique for reducing Data dimensionality for Databases drawn from smooth distributions.

Moritz Hardt - One of the best experts on this subject based on the ideXlab platform.

  • FOCS - A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
    2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010
    Co-Authors: Moritz Hardt, Guy N. Rothblum
    Abstract:

    We consider statistical Data analysis in the interactive setting. In this setting a trusted curator maintains a Database of sensitive information about individual participants, and releases privacy-preserving answers to queries as they arrive. Our primary contribution is a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) queries that arrive online and may be adaptively chosen. This is the first mechanism with worst-case accuracy guarantees that can answer large numbers of interactive queries and is {\em efficient} (in terms of the runtime's dependence on the Data universe size). The error is asymptotically \emph{optimal} in its dependence on the number of participants, and depends only logarithmically on the number of queries being answered. The running time is nearly {\em linear} in the size of the Data universe. As a further contribution, when we relax the utility requirement and require accuracy only for Databases drawn from a rich class of Databases, we obtain exponential improvements in running time. Even in this relaxed setting we continue to guarantee privacy for {\em any} input Database. Only the utility requirement is relaxed. Specifically, we show that when the input Database is drawn from a {\em smooth} distribution — a distribution that does not place too much weight on any Single Data Item — accuracy remains as above, and the running time becomes {\em poly-logarithmic} in the Data universe size. The main technical contributions are the application of multiplicative weights techniques to the differential privacy setting, a new privacy analysis for the interactive setting, and a technique for reducing Data dimensionality for Databases drawn from smooth distributions.

  • A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
    2010 IEEE 51st Annual Symposium on Foundations of Computer Science, 2010
    Co-Authors: Moritz Hardt, Guy N. Rothblum
    Abstract:

    We consider statistical Data analysis in the interactive setting. In this setting a trusted curator maintains a Database of sensitive information about individual participants, and releases privacy-preserving answers to queries as they arrive. Our primary contribution is a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) queries that arrive online and may be adaptively chosen. This is the first mechanism with worst-case accuracy guarantees that can answer large numbers of interactive queries and is efficient (in terms of the runtime's dependence on the Data universe size). The error is asymptotically optimal in its dependence on the number of participants, and depends only logarithmically on the number of queries being answered. The running time is nearly linear in the size of the Data universe. As a further contribution, when we relax the utility requirement and require accuracy only for Databases drawn from a rich class of Databases, we obtain exponential improvements in running time. Even in this relaxed setting we continue to guarantee privacy for any input Database. Only the utility requirement is relaxed. Specifically, we show that when the input Database is drawn from a smooth distribution - a distribution that does not place too much weight on any Single Data Item - accuracy remains as above, and the running time becomes poly-logarithmic in the Data universe size. The main technical contributions are the application of multiplicative weights techniques to the differential privacy setting, a new privacy analysis for the interactive setting, and a technique for reducing Data dimensionality for Databases drawn from smooth distributions.

E. Chan - One of the best experts on this subject based on the ideXlab platform.

  • RTCSA - Adaptive Data broadcast strategy for transactions with multiple Data requests in mobile computing environments
    Proceedings Sixth International Conference on Real-Time Computing Systems and Applications. RTCSA'99 (Cat. No.PR00306), 1999
    Co-Authors: Joe Yuen, E. Chan
    Abstract:

    Data broadcast in mobile environments has received much attention in recent years and a large number of algorithms have been proposed. However, existing work in the literature only assume a Single Data Item per transaction. In this paper we propose an adaptive Data broadcast strategy designed for the case where there may be more than one Data Item per transaction, for a mobile environment with both push and pull channels. The basic rationale of the strategy is to increase the number of transactions that can be completed by improving the use of the on-demand channel. Extensive simulation shows that the algorithm performs well in a variety of operating environments.

  • Adaptive Data broadcast strategy for transactions with multiple Data requests in mobile computing environments
    Proceedings Sixth International Conference on Real-Time Computing Systems and Applications. RTCSA'99 (Cat. No.PR00306), 1999
    Co-Authors: J.c.-h. Yuen, E. Chan
    Abstract:

    Data broadcast in mobile environments has received much attention in recent years and a large number of algorithms have been proposed. However, existing work in the literature only assume a Single Data Item per transaction. In this paper we propose an adaptive Data broadcast strategy designed for the case where there may be more than one Data Item per transaction, for a mobile environment with both push and pull channels. The basic rationale of the strategy is to increase the number of transactions that can be completed by improving the use of the on-demand channel. Extensive simulation shows that the algorithm performs well in a variety of operating environments.

P.t. Gaughan - One of the best experts on this subject based on the ideXlab platform.

  • SPDP - Data streaming: very low overhead communication for fine-grained multicomputing
    Proceedings.Seventh IEEE Symposium on Parallel and Distributed Processing, 1995
    Co-Authors: P.t. Gaughan
    Abstract:

    Recent developments have greatly reduced network latencies in multiprocessor networks. Thus, software overhead is becoming the primary cost of multiprocessor communication. This paper proposes Data streaming-a technique which places explicit send and receive instructions in the user code-as a means to cut software overhead to a minimum. Data streaming has the added benefit that it can tighten the coupling between processors by reducing the message size to that of a Single Data Item. This paper presents experimental results that indicate Data streaming can cut software overhead to less than one instruction per byte of Data transmitted.

  • Data streaming: very low overhead communication for fine-grained multicomputing
    Proceedings.Seventh IEEE Symposium on Parallel and Distributed Processing, 1995
    Co-Authors: P.t. Gaughan
    Abstract:

    Recent developments have greatly reduced network latencies in multiprocessor networks. Thus, software overhead is becoming the primary cost of multiprocessor communication. This paper proposes Data streaming-a technique which places explicit send and receive instructions in the user code-as a means to cut software overhead to a minimum. Data streaming has the added benefit that it can tighten the coupling between processors by reducing the message size to that of a Single Data Item. This paper presents experimental results that indicate Data streaming can cut software overhead to less than one instruction per byte of Data transmitted.

M. Minkoff - One of the best experts on this subject based on the ideXlab platform.

  • FOCS - Building Steiner trees with incomplete global knowledge
    Proceedings 41st Annual Symposium on Foundations of Computer Science, 2000
    Co-Authors: D.r. Karget, M. Minkoff
    Abstract:

    A networking problem of present-day interest is that of distributing a Single Data Item to multiple clients while minimizing network usage. Steiner tree algorithms are a natural solution method, but only when the set of clients requesting the Data is known. We study what can be done without this global knowledge, when a given vertex knows only the probability that any other client wishes to be connected, and must simply specify a fixed path to the Data to be used in case it is requested. Our problem is an example of a class of network design problems with concave cost functions (which arise when the design problem exhibits economies of scale). In order to solve our problem, we introduce a new version of the facility location problem: one in which every open facility is required to have some minimum amount of demand assigned to it. We present a simple bicriterion approximation for this problem, one which is loose in both assignment cost and minimum demand, but within a constant factor of the optimum for both. This suffices for our application. We leave open the question of finding an algorithm that produces a truly feasible approximate solution.

  • Building Steiner trees with incomplete global knowledge
    Proceedings 41st Annual Symposium on Foundations of Computer Science, 2000
    Co-Authors: M. Minkoff
    Abstract:

    A networking problem of present-day interest is that of distributing a Single Data Item to multiple clients while minimizing network usage. Steiner tree algorithms are a natural solution method, but only when the set of clients requesting the Data is known. We study what can be done without this global knowledge, when a given vertex knows only the probability that any other client wishes to be connected, and must simply specify a fixed path to the Data to be used in case it is requested. Our problem is an example of a class of network design problems with concave cost functions (which arise when the design problem exhibits economies of scale). In order to solve our problem, we introduce a new version of the facility location problem: one in which every open facility is required to have some minimum amount of demand assigned to it. We present a simple bicriterion approximation for this problem, one which is loose in both assignment cost and minimum demand, but within a constant factor of the optimum for both. This suffices for our application. We leave open the question of finding an algorithm that produces a truly feasible approximate solution.