The Experts below are selected from a list of 52926 Experts worldwide ranked by ideXlab platform

Tsz Hong Hubert Chan - One of the best experts on this subject based on the ideXlab platform.

  • privacy preserving aggregation of time series data
    Network and Distributed System Security Symposium, 2011
    Co-Authors: Runting Shi, Richard Chow, Tsz Hong Hubert Chan
    Abstract:

    A private stream aggregation (PSA) system contributes a user's data to a data aggregator without compromising the user's privacy. The system can begin by determining a private key for a local user in a set of users, wherein the sum of the private keys associated with the set of users and the data aggregator is equal to zero. The system also selects a set of data Values associated with the local user. Then, the system encrypts individual data Values in the set based in part on the private key to produce a set of encrypted data Values, thereby allowing the data aggregator to decrypt an Aggregate Value across the set of users without decrypting individual data Values associated with the set of users, and without interacting with the set of users while decrypting the Aggregate Value. The system also sends the set of encrypted data Values to the data aggregator.

Alonso Silva - One of the best experts on this subject based on the ideXlab platform.

  • Approximate Equilibria in Non-constant-sum Colonel Blotto and Lottery Blotto Games with Large Numbers of Battlefields
    2019
    Co-Authors: Dong Quan Vu, Patrick Loiseau, Alonso Silva
    Abstract:

    In the Colonel Blotto game, two players with a fixed budget simultaneously allocate their resources across n battlefields to maximize the Aggregate Value gained from the battlefields where they have the higher allocation. Despite its long-standing history and important applicability, the Colonel Blotto game still lacks a complete Nash equilibrium characterization in its most general form-the non-constant-sum version with asymmetric players and heterogeneous battlefields. In this work, we propose a simply-constructed class of strategies-the independently uniform strategies-and we prove them to be approximate equilibria of the non-constant-sum Colonel Blotto game; moreover, we also characterize the approximation error according to the game's parameters. We also introduce an extension called the Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contexts. We prove that the proposed strategies are also approximate equilibria of the Lottery Blotto game.

  • Efficient Computation of Approximate Equilibria in Discrete Colonel Blotto Games
    2018
    Co-Authors: Patrick Loiseau, Alonso Silva
    Abstract:

    The Colonel Blotto game is a famous game commonly used to model resource allocation problems in many domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the Aggregate Value of battlefields they win, each battlefield being won by the player who allocates more resources to it. The continuous version of the game-where players can choose any fractional allocation-has been extensively studied , albeit only with partial results to date. Recently , the discrete version-where allocations can only be integers-started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters, in particular number of battlefields and resource budgets. We also propose an efficient dynamic programming algorithm to compute the best-response to any strategy that allows computing for each game instance the actual Value of the error. We perform numerical experiments that show that the proposed strategy provides a fast and good approximation to the equilibrium even for moderate numbers of battlefields.

  • A simple and efficient algorithm to compute epsilon-equilibria of discrete Colonel Blotto games
    2018
    Co-Authors: Alonso Silva, Dong Quan Vu, Patrick Loiseau
    Abstract:

    The Colonel Blotto game is a famous game commonly used to model resource allocation problems in domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the Aggregate Value of battlefields they win, each battlefield being won by the player who allocates more resources to it. Recently, the discrete version of the game-where allocations can only be integers-started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters. Through numerical experiments, we show that the proposed strategy provides a fast and good approximation even for moderate numbers of battlefields.

  • Efficient computation of approximate equilibria in discrete Colonel Blotto games
    2018
    Co-Authors: Patrick Loiseau, Alonso Silva
    Abstract:

    The Colonel Blotto game is a famous game commonly used to model resource allocation problems in many domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the Aggregate Value of battlefields they win, each battlefield being won by the player who allocates more resources to it. The continuous version of the game—where players can choose any fractional allocation—has been extensively studied, albeit only with partial results to date. Recently, the discrete version—where allocations can only be integers—started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters. We also propose an efficient dynamic programming algorithm in order to compute for each game instance the actual Value of the error. We perform numerical experiments that show that the proposed strategy provides a fast and good approximation to the equilibrium even for moderate numbers of battlefields.

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

  • privacy preserving aggregation of time series data
    Network and Distributed System Security Symposium, 2011
    Co-Authors: Runting Shi, Richard Chow, Tsz Hong Hubert Chan
    Abstract:

    A private stream aggregation (PSA) system contributes a user's data to a data aggregator without compromising the user's privacy. The system can begin by determining a private key for a local user in a set of users, wherein the sum of the private keys associated with the set of users and the data aggregator is equal to zero. The system also selects a set of data Values associated with the local user. Then, the system encrypts individual data Values in the set based in part on the private key to produce a set of encrypted data Values, thereby allowing the data aggregator to decrypt an Aggregate Value across the set of users without decrypting individual data Values associated with the set of users, and without interacting with the set of users while decrypting the Aggregate Value. The system also sends the set of encrypted data Values to the data aggregator.

Patrick Loiseau - One of the best experts on this subject based on the ideXlab platform.

  • Approximate Equilibria in Non-constant-sum Colonel Blotto and Lottery Blotto Games with Large Numbers of Battlefields
    2019
    Co-Authors: Dong Quan Vu, Patrick Loiseau, Alonso Silva
    Abstract:

    In the Colonel Blotto game, two players with a fixed budget simultaneously allocate their resources across n battlefields to maximize the Aggregate Value gained from the battlefields where they have the higher allocation. Despite its long-standing history and important applicability, the Colonel Blotto game still lacks a complete Nash equilibrium characterization in its most general form-the non-constant-sum version with asymmetric players and heterogeneous battlefields. In this work, we propose a simply-constructed class of strategies-the independently uniform strategies-and we prove them to be approximate equilibria of the non-constant-sum Colonel Blotto game; moreover, we also characterize the approximation error according to the game's parameters. We also introduce an extension called the Lottery Blotto game, with stochastic winner-determination rules allowing more flexibility in modeling practical contexts. We prove that the proposed strategies are also approximate equilibria of the Lottery Blotto game.

  • Efficient Computation of Approximate Equilibria in Discrete Colonel Blotto Games
    2018
    Co-Authors: Patrick Loiseau, Alonso Silva
    Abstract:

    The Colonel Blotto game is a famous game commonly used to model resource allocation problems in many domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the Aggregate Value of battlefields they win, each battlefield being won by the player who allocates more resources to it. The continuous version of the game-where players can choose any fractional allocation-has been extensively studied , albeit only with partial results to date. Recently , the discrete version-where allocations can only be integers-started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters, in particular number of battlefields and resource budgets. We also propose an efficient dynamic programming algorithm to compute the best-response to any strategy that allows computing for each game instance the actual Value of the error. We perform numerical experiments that show that the proposed strategy provides a fast and good approximation to the equilibrium even for moderate numbers of battlefields.

  • A simple and efficient algorithm to compute epsilon-equilibria of discrete Colonel Blotto games
    2018
    Co-Authors: Alonso Silva, Dong Quan Vu, Patrick Loiseau
    Abstract:

    The Colonel Blotto game is a famous game commonly used to model resource allocation problems in domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the Aggregate Value of battlefields they win, each battlefield being won by the player who allocates more resources to it. Recently, the discrete version of the game-where allocations can only be integers-started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters. Through numerical experiments, we show that the proposed strategy provides a fast and good approximation even for moderate numbers of battlefields.

  • Efficient computation of approximate equilibria in discrete Colonel Blotto games
    2018
    Co-Authors: Patrick Loiseau, Alonso Silva
    Abstract:

    The Colonel Blotto game is a famous game commonly used to model resource allocation problems in many domains ranging from security to advertising. Two players distribute a fixed budget of resources on multiple battlefields to maximize the Aggregate Value of battlefields they win, each battlefield being won by the player who allocates more resources to it. The continuous version of the game—where players can choose any fractional allocation—has been extensively studied, albeit only with partial results to date. Recently, the discrete version—where allocations can only be integers—started to gain traction and algorithms were proposed to compute the equilibrium in polynomial time; but these remain computationally impractical for large (or even moderate) numbers of battlefields. In this paper, we propose an algorithm to compute very efficiently an approximate equilibrium for the discrete Colonel Blotto game with many battlefields. We provide a theoretical bound on the approximation error as a function of the game's parameters. We also propose an efficient dynamic programming algorithm in order to compute for each game instance the actual Value of the error. We perform numerical experiments that show that the proposed strategy provides a fast and good approximation to the equilibrium even for moderate numbers of battlefields.

Richard Chow - One of the best experts on this subject based on the ideXlab platform.

  • privacy preserving aggregation of time series data
    Network and Distributed System Security Symposium, 2011
    Co-Authors: Runting Shi, Richard Chow, Tsz Hong Hubert Chan
    Abstract:

    A private stream aggregation (PSA) system contributes a user's data to a data aggregator without compromising the user's privacy. The system can begin by determining a private key for a local user in a set of users, wherein the sum of the private keys associated with the set of users and the data aggregator is equal to zero. The system also selects a set of data Values associated with the local user. Then, the system encrypts individual data Values in the set based in part on the private key to produce a set of encrypted data Values, thereby allowing the data aggregator to decrypt an Aggregate Value across the set of users without decrypting individual data Values associated with the set of users, and without interacting with the set of users while decrypting the Aggregate Value. The system also sends the set of encrypted data Values to the data aggregator.