The Experts below are selected from a list of 6894 Experts worldwide ranked by ideXlab platform

Ken Barker - One of the best experts on this subject based on the ideXlab platform.

  • towards breaking the curse of dimensionality for high dimensional privacy an extended version
    2014
    Co-Authors: Hessam Zakerzadeh, Charu C Aggarwal, Ken Barker
    Abstract:

    The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-Attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, l-diversity, and t-closeness models. In the presence of inter-Attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent Identity, Attribute, and membership disclosure attack.

  • towards breaking the curse of dimensionality for high dimensional privacy
    2014
    Co-Authors: Hessam Zakerzadeh, Charu C Aggarwal, Ken Barker
    Abstract:

    The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-Attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, `-diversity, and t-closeness models. In the presence of inter-Attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent Identity, Attribute, and membership

Hessam Zakerzadeh - One of the best experts on this subject based on the ideXlab platform.

  • towards breaking the curse of dimensionality for high dimensional privacy an extended version
    2014
    Co-Authors: Hessam Zakerzadeh, Charu C Aggarwal, Ken Barker
    Abstract:

    The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-Attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, l-diversity, and t-closeness models. In the presence of inter-Attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent Identity, Attribute, and membership disclosure attack.

  • towards breaking the curse of dimensionality for high dimensional privacy
    2014
    Co-Authors: Hessam Zakerzadeh, Charu C Aggarwal, Ken Barker
    Abstract:

    The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-Attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, `-diversity, and t-closeness models. In the presence of inter-Attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent Identity, Attribute, and membership

Charu C Aggarwal - One of the best experts on this subject based on the ideXlab platform.

  • towards breaking the curse of dimensionality for high dimensional privacy an extended version
    2014
    Co-Authors: Hessam Zakerzadeh, Charu C Aggarwal, Ken Barker
    Abstract:

    The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-Attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, l-diversity, and t-closeness models. In the presence of inter-Attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent Identity, Attribute, and membership disclosure attack.

  • towards breaking the curse of dimensionality for high dimensional privacy
    2014
    Co-Authors: Hessam Zakerzadeh, Charu C Aggarwal, Ken Barker
    Abstract:

    The curse of dimensionality has remained a challenge for a wide variety of algorithms in data mining, clustering, classification and privacy. Recently, it was shown that an increasing dimensionality makes the data resistant to effective privacy. The theoretical results seem to suggest that the dimensionality curse is a fundamental barrier to privacy preservation. However, in practice, we show that some of the common properties of real data can be leveraged in order to greatly ameliorate the negative effects of the curse of dimensionality. In real data sets, many dimensions contain high levels of inter-Attribute correlations. Such correlations enable the use of a process known as vertical fragmentation in order to decompose the data into vertical subsets of smaller dimensionality. An information-theoretic criterion of mutual information is used in the vertical decomposition process. This allows the use of an anonymization process, which is based on combining results from multiple independent fragments. We present a general approach which can be applied to the k-anonymity, `-diversity, and t-closeness models. In the presence of inter-Attribute correlations, such an approach continues to be much more robust in higher dimensionality, without losing accuracy. We present experimental results illustrating the effectiveness of the approach. This approach is resilient enough to prevent Identity, Attribute, and membership

Elisa Bertino - One of the best experts on this subject based on the ideXlab platform.

  • privacy preserving Identity Attribute verification in windows cardspace
    2010
    Co-Authors: Kevin Steuer, Ruchith Fernando, Elisa Bertino
    Abstract:

    There are various Identity management systems in place today. The way these share information about entities that interact with them gives rise to various privacy issues. This paper addresses two main such issues in Windows CardSpace, regarding the trust users' have to place on the system with respect to their personal information and to protect them against being exploited by means such as profiling. The protocol extensions proposed were implemented with a prototype as a proof of concept.

  • Identity Attribute based role provisioning for human ws bpel processes
    2009
    Co-Authors: Federica Paci, Rodolfo Ferrini, Elisa Bertino
    Abstract:

    The WS-BPEL specification focuses on business processes the activities of which are assumed to be interactions with Web services. However, WS-BPEL processes go beyond the orchestration of activities exposed as Web services. There are cases in which people must be considered as additional participants to the execution of a process. The inclusion of humans, in turn, requires solutions to support the specification and enforcement of authorizations to users for the execution of human activities while enforcing authorization constraints.In this paper, we extend RBAC-WS-BPEL, a role-based authorization framework for WS-BPEL processes with an Identity Attribute-based role provisioning approach that preserves the privacy of the users who claim the execution of human activities. Such approach is based on the notion of Identity records and role provisioning policies, and uses Pedersen commitments, aggregated zero knowledge proof of knowledge, and Oblivious Commitment-Based Envelope protocols to achieve privacy of user Identity information.

  • a federated digital Identity management approach for business processes
    2008
    Co-Authors: Elisa Bertino, Federica Paci, Rodolfo Ferrini, Andrea Musci, Kevin Steuer
    Abstract:

    Business processes have gained a lot of attention because of the pressing need for integrating existing resources and services to better fulfill customer needs. A key feature of business processes is that they are built from composable services, referred to as component services, that may belong to different domains. In such a context, flexible multi-domain Identity management solutions are crucial for increased security and user-convenience. In particular, it is important that during the execution of a business process the component services be able to verify the Identity of the client to check that it has the required permissions for accessing the services. To address the problem of multi-domain Identity management, we propose a multi-factor Identity Attribute verification protocol for business processes that assures clients privacy and handles naming heterogeneity.

Kevin Steuer - One of the best experts on this subject based on the ideXlab platform.

  • privacy preserving Identity Attribute verification in windows cardspace
    2010
    Co-Authors: Kevin Steuer, Ruchith Fernando, Elisa Bertino
    Abstract:

    There are various Identity management systems in place today. The way these share information about entities that interact with them gives rise to various privacy issues. This paper addresses two main such issues in Windows CardSpace, regarding the trust users' have to place on the system with respect to their personal information and to protect them against being exploited by means such as profiling. The protocol extensions proposed were implemented with a prototype as a proof of concept.

  • a federated digital Identity management approach for business processes
    2008
    Co-Authors: Elisa Bertino, Federica Paci, Rodolfo Ferrini, Andrea Musci, Kevin Steuer
    Abstract:

    Business processes have gained a lot of attention because of the pressing need for integrating existing resources and services to better fulfill customer needs. A key feature of business processes is that they are built from composable services, referred to as component services, that may belong to different domains. In such a context, flexible multi-domain Identity management solutions are crucial for increased security and user-convenience. In particular, it is important that during the execution of a business process the component services be able to verify the Identity of the client to check that it has the required permissions for accessing the services. To address the problem of multi-domain Identity management, we propose a multi-factor Identity Attribute verification protocol for business processes that assures clients privacy and handles naming heterogeneity.