The Experts below are selected from a list of 151116 Experts worldwide ranked by ideXlab platform

Salil P Vadhan - One of the best experts on this subject based on the ideXlab platform.

  • separating computational and statistical differential privacy in the client server Model
    Theory of Cryptography Conference, 2016
    Co-Authors: Mark Bun, Yihsiu Chen, Salil P Vadhan
    Abstract:

    Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any possibly adversarial data analyst is unable to learn too much information that is specific to an individual. Mironov et al.i¾?CRYPTO 2009 proposed several computational relaxations of differential privacy CDP, which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional Client-Server Model of data analysis. In fact, Groce, Katz and Yerukhimovichi¾?TCC 2011 showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs zaps for $$\mathbf {NP}$$ , that there is in fact a computational task in the Client-Server Model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.

  • TCC (B1) - Separating Computational and Statistical Differential Privacy in the Client-Server Model
    Theory of Cryptography, 2016
    Co-Authors: Mark Bun, Yihsiu Chen, Salil P Vadhan
    Abstract:

    Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any possibly adversarial data analyst is unable to learn too much information that is specific to an individual. Mironov et al.i¾?CRYPTO 2009 proposed several computational relaxations of differential privacy CDP, which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional Client-Server Model of data analysis. In fact, Groce, Katz and Yerukhimovichi¾?TCC 2011 showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs zaps for $$\mathbf {NP}$$ , that there is in fact a computational task in the Client-Server Model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.

Mark Bun - One of the best experts on this subject based on the ideXlab platform.

  • separating computational and statistical differential privacy in the client server Model
    Theory of Cryptography Conference, 2016
    Co-Authors: Mark Bun, Yihsiu Chen, Salil P Vadhan
    Abstract:

    Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any possibly adversarial data analyst is unable to learn too much information that is specific to an individual. Mironov et al.i¾?CRYPTO 2009 proposed several computational relaxations of differential privacy CDP, which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional Client-Server Model of data analysis. In fact, Groce, Katz and Yerukhimovichi¾?TCC 2011 showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs zaps for $$\mathbf {NP}$$ , that there is in fact a computational task in the Client-Server Model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.

  • TCC (B1) - Separating Computational and Statistical Differential Privacy in the Client-Server Model
    Theory of Cryptography, 2016
    Co-Authors: Mark Bun, Yihsiu Chen, Salil P Vadhan
    Abstract:

    Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any possibly adversarial data analyst is unable to learn too much information that is specific to an individual. Mironov et al.i¾?CRYPTO 2009 proposed several computational relaxations of differential privacy CDP, which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional Client-Server Model of data analysis. In fact, Groce, Katz and Yerukhimovichi¾?TCC 2011 showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs zaps for $$\mathbf {NP}$$ , that there is in fact a computational task in the Client-Server Model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.

Yihsiu Chen - One of the best experts on this subject based on the ideXlab platform.

  • separating computational and statistical differential privacy in the client server Model
    Theory of Cryptography Conference, 2016
    Co-Authors: Mark Bun, Yihsiu Chen, Salil P Vadhan
    Abstract:

    Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any possibly adversarial data analyst is unable to learn too much information that is specific to an individual. Mironov et al.i¾?CRYPTO 2009 proposed several computational relaxations of differential privacy CDP, which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional Client-Server Model of data analysis. In fact, Groce, Katz and Yerukhimovichi¾?TCC 2011 showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs zaps for $$\mathbf {NP}$$ , that there is in fact a computational task in the Client-Server Model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.

  • TCC (B1) - Separating Computational and Statistical Differential Privacy in the Client-Server Model
    Theory of Cryptography, 2016
    Co-Authors: Mark Bun, Yihsiu Chen, Salil P Vadhan
    Abstract:

    Differential privacy is a mathematical definition of privacy for statistical data analysis. It guarantees that any possibly adversarial data analyst is unable to learn too much information that is specific to an individual. Mironov et al.i¾?CRYPTO 2009 proposed several computational relaxations of differential privacy CDP, which relax this guarantee to hold only against computationally bounded adversaries. Their work and subsequent work showed that CDP can yield substantial accuracy improvements in various multiparty privacy problems. However, these works left open whether such improvements are possible in the traditional Client-Server Model of data analysis. In fact, Groce, Katz and Yerukhimovichi¾?TCC 2011 showed that, in this setting, it is impossible to take advantage of CDP for many natural statistical tasks. Our main result shows that, assuming the existence of sub-exponentially secure one-way functions and 2-message witness indistinguishable proofs zaps for $$\mathbf {NP}$$ , that there is in fact a computational task in the Client-Server Model that can be efficiently performed with CDP, but is infeasible to perform with information-theoretic differential privacy.

Laurent Réveillère - One of the best experts on this subject based on the ideXlab platform.

  • A programmable Client-Server Model: Robust extensibility via DSLs
    2003
    Co-Authors: Charles Consel, Laurent Réveillère
    Abstract:

    The Client-Server Model has been successfully used to support a wide variety of families of services in the context of distributed systems. However, its server-centric nature makes it insensitive to fast changing client characteristics like terminal capabilities, network features, user preferences and evolving needs. To overcome this key limitation, we present an approach to enabling a server to adapt to different clients by making it programmable. A service-description language is used to program server adaptations. This language is designed as a domain-specific language to offer expressiveness and conciseness without compromising safety and security. We show that our approach makes servers adaptable without requiring the deployment of new protocols or server implementations. We illustrate our approach with the Internet Message Access Protocol (IMAP). An IMAP server is made programmable and a language, named Pems, is introduced to program robust variations of e-mail services. Our approach is uniformly used to develop a platform for multimedia communication services. This platform is composed of programmable servers for telephony services, e-mail processing, remote-document processing and stream adapters.

  • ASE - A programmable Client-Server Model: robust extensibility via DSLs
    18th IEEE International Conference on Automated Software Engineering 2003. Proceedings., 1
    Co-Authors: Charles Consel, Laurent Réveillère
    Abstract:

    The Client-Server Model has been successfully used to support a wide variety of families of services in the context of distributed systems. However, its server-centric nature makes it insensitive to fast changing client characteristics like terminal capabilities, network features, user preferences and evolving needs. To overcome this key limitation, we present an approach to enabling a server to adapt to different clients by making it programmable. A service-description language is used to program server adaptations. This language is designed as a domain-specific language to offer expressiveness and conciseness without compromising safety and security. We show that our approach makes servers adaptable without requiring the deployment of new protocols or server implementations. We illustrate our approach with the Internet Message Access Protocol (IMAP). An IMAP server is made programmable and a language, named Pems, is introduced to program robust variations of e-mail services. Our approach is uniformly used to develop a platform for multimedia communication services. This platform is composed of programmable servers for telephony services, e-mail processing, remote-document processing and stream adapters.

Stéphane Letz - One of the best experts on this subject based on the ideXlab platform.

  • Real-Time IPC on a client / server Model: Multiple OS performances benchmark
    2001
    Co-Authors: Dominique Fober, Yann Orlarey, Stéphane Letz
    Abstract:

    This paper presents inter processus communication (IPC) real-time performances measured on different operating systems, including GNU/Linux, Windows 98, 2000, NT 4.0 and MacOS X. The adopted point of view is based on a client / server Model. The operating systems behavior and message transmission latency times are evaluated in different contexts: with one to ten clients for the server, with systems more or less busy with alternate tasks. As we wanted to measure real world performances, the benchmarks have been applied to operating systems running standard default configurations. Each time it was possible, we compared the different systems on the base of local Unix sockets communication way. But above all, we choose the most efficient communication way per system to evaluate the overall best performances that one can expect in a client / server Model.

  • Real-Time IPC on a client / server Model: Multiple OS performances benchmark
    2001
    Co-Authors: Dominique Fober, Yann Orlarey, Stéphane Letz
    Abstract:

    This paper presents inter processus communication (IPC) real-time performances measured on different operating systems, including GNU/Linux, Windows 98, 2000, NT 4.0 and MacOS X. The adopted point of view is based on a client / server Model. The operating systems behavior and message transmission latency times are evaluated in different contexts: with one to ten clients for the server, with systems more or less busy with alternate tasks. As we wanted to measure real world performances, the benchmarks have been applied to operating systems running standard default configurations. Each time it was possible, we compared the different systems on the base of local Unix sockets communication way. But above all, we choose the most efficient communication way per system to evaluate the overall best performances that one can expect in a client / server Model.