The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Sen Wang - One of the best experts on this subject based on the ideXlab platform.

  • Collective Protection: Preventing Sensitive Inferences via Integrative Transformation
    2019 IEEE International Conference on Data Mining (ICDM), 2019
    Co-Authors: Dalin Zhang, Kaixuan Chen, Guodong Long, Sen Wang
    Abstract:

    Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic Information that can be inferred from the time series sensor data. Existing sensitive Information Protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive Information for training. Considering this gap, we propose a novel user sensitive Information Protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive Information. The presented approach transforms raw sensor data into a new format that has a "style" (sensitive Information) of random noise and a "content" (desired Information) of the raw sensor data, thus is free of user sensitive Information for training and able to collectively protect all sensitive Information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the Protection for all sensitive Information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.

Dalin Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Collective Protection: Preventing Sensitive Inferences via Integrative Transformation
    2019 IEEE International Conference on Data Mining (ICDM), 2019
    Co-Authors: Dalin Zhang, Kaixuan Chen, Guodong Long, Sen Wang
    Abstract:

    Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic Information that can be inferred from the time series sensor data. Existing sensitive Information Protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive Information for training. Considering this gap, we propose a novel user sensitive Information Protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive Information. The presented approach transforms raw sensor data into a new format that has a "style" (sensitive Information) of random noise and a "content" (desired Information) of the raw sensor data, thus is free of user sensitive Information for training and able to collectively protect all sensitive Information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the Protection for all sensitive Information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.

Carmelo Pierpaolo Parello - One of the best experts on this subject based on the ideXlab platform.

  • proprietary Information Protection and endogenous technological change the long run implications of industrial espionage
    RIVISTA DI POLITICA ECONOMICA, 2005
    Co-Authors: Carmelo Pierpaolo Parello
    Abstract:

    This paper deals with proprietary Information and industrial espionage. To obtain this goal, an innovation-based growth model is constructed where R&D employment is split into two types of researchers: inventors and spies. The paper provides an analysis of the steady-state effects of better enforcement of proprietary Information Protection in terms of a change of the institutional set-up devoted to intellectual property rights and private Information Protection. We find that there is only a temporary positive impact on the innovation rate, while there is permanent negative effect on the steady-state rate of spying and nominal wage.

  • proprietary Information Protection and the long run implications of industrial espionage
    Rivista di Politica Economica, 2005
    Co-Authors: Carmelo Pierpaolo Parello
    Abstract:

    This paper deals with proprietary Information and industrial espionage. To obtain this goal, an innovation-based growth model is constructed where R&D employment is split into two types of researchers: inventors and spies. The paper provides an analysis of the steady-state effects of better enforcement of proprietary Information Protection in terms of a change of the institutional set-up devoted to intellectual property rights and private Information Protection. We find that there is only a temporary positive impact on the innovation rate, while there is permanent negative effect on the steady-state rate of spying and nominal wage.

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

  • Collective Protection: Preventing Sensitive Inferences via Integrative Transformation
    2019 IEEE International Conference on Data Mining (ICDM), 2019
    Co-Authors: Dalin Zhang, Kaixuan Chen, Guodong Long, Sen Wang
    Abstract:

    Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic Information that can be inferred from the time series sensor data. Existing sensitive Information Protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive Information for training. Considering this gap, we propose a novel user sensitive Information Protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive Information. The presented approach transforms raw sensor data into a new format that has a "style" (sensitive Information) of random noise and a "content" (desired Information) of the raw sensor data, thus is free of user sensitive Information for training and able to collectively protect all sensitive Information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the Protection for all sensitive Information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.

Guodong Long - One of the best experts on this subject based on the ideXlab platform.

  • Collective Protection: Preventing Sensitive Inferences via Integrative Transformation
    2019 IEEE International Conference on Data Mining (ICDM), 2019
    Co-Authors: Dalin Zhang, Kaixuan Chen, Guodong Long, Sen Wang
    Abstract:

    Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks of leaking physical and demographic Information that can be inferred from the time series sensor data. Existing sensitive Information Protection mechanisms that depend on data transformation are effective only on a particular sensitive attribute, together with usually requiring the labels of sensitive Information for training. Considering this gap, we propose a novel user sensitive Information Protection framework without using a sensitive training dataset or being validated on protecting only one specific sensitive Information. The presented approach transforms raw sensor data into a new format that has a "style" (sensitive Information) of random noise and a "content" (desired Information) of the raw sensor data, thus is free of user sensitive Information for training and able to collectively protect all sensitive Information at once. Our implementation and experiments on two real-world multisensor human activity datasets demonstrate that the proposed data transformation technique can achieve the Protection for all sensitive Information at once without requiring the knowledge of users' personal attributes for training, and simultaneously preserve the usability of the new transformed data with regard to inferring human activities with insignificant performance loss.