The Experts below are selected from a list of 159 Experts worldwide ranked by ideXlab platform

Juggapong Natwichai - One of the best experts on this subject based on the ideXlab platform.

  • Data anonymization: a novel optimal k-anonymity algorithm for identical Generalization Hierarchy data in IoT
    Service Oriented Computing and Applications, 2020
    Co-Authors: Waranya Mahanan, W. Art Chaovalitwongse, Juggapong Natwichai
    Abstract:

    Advancement in the Internet of Things (IoT) technologies makes life more convenient for people. Data sensed from the devices can be used for analyzing and responding to people’s needs seamlessly. An important consequence of such convenience is that privacy protection becomes a very important issue to be addressed effectively. Various data anonymization model has been proposed for such issue—one of the most widely applied models is the k -anonymity. The k -anonymity prevents the re-identification by replacing the input data with its more general form for transforming the data to have at least k identical tuples. In this paper, we focus on a special case of the input datasets which all the quasi-identifiers, the linkable attributes in the dataset, have identical data types, so-called identical Generalization Hierarchy (IGH). The solutions for such case will be applicable effectively to address the general IoT data privacy protection due to its data nature. We proposed a novel method to provide a globally optimized k -anonymity solution for the IGH datasets. The proposed algorithms determine an optimal solution based on the characteristics of the IGH data by visiting and evaluating only essential nodes of Generalization lattice that satisfy the k -anonymity. Since the k -anonymization problem is an NP-hard, we show that our algorithm can efficiently find an optimal k -anonymity solutions with exploiting such special characteristics of the IGH data, i.e., the optimality between the nodes in different levels of Generalization lattice. From the experimental results, it is obvious that our algorithm is much more efficient than the comparative algorithms by less searching on the given lattice.

  • NBiS - Characterizations of Local Recoding Method on k-Anonymity
    Advances in Network-Based Information Systems, 2018
    Co-Authors: Waranya Mahanan, Juggapong Natwichai, W. Art Chaovalitwongse
    Abstract:

    k-Anonymity is one of the most widely used techniques for protecting the privacy of the publishing datasets by making each individual not distinguished from at least k-1 other individuals. The local recoding method is an approach to achieve k-anonymization through suppression and Generalization. The method generalizes the dataset at the cell level. Therefore, the local recoding could achieve the k-anonymization with only a small distortion. As the optimal k-anonymity has been proved as the NP-hard problem, the plenty of optimal algorithm local recoding has been proposed. In this research, we study the characteristics of the local recoding method. In addition, we discover the special characteristic dataset that all Generalization hierarchies of each quasi-identifier are identical, called an “Identical Generalization Hierarchy” (IGH) data. We also compare the efficiency of the well-known algorithms of the local recoding method on both \(non-IGH\) and IGH data.

Kokou Yétongnon - One of the best experts on this subject based on the ideXlab platform.

  • Interoperability of B2B Applications: Methods and Tools
    Advances in Database Research, 2005
    Co-Authors: Christophe Nicolle, Kokou Yétongnon, Jean-claude Simon
    Abstract:

    This paper presents a Web-based data integration methodology and tool framework, called X-TIME, for the development of Business-to-Business (B2B) design environments and applications. X-TIME provides a data model translator toolkit based on an extensible meta model and XML. It allows the creation of adaptable semantic-oriented meta models to support the design of wrappers or reconciliators (mediators), taking into account characteristics of interoperable information systems, such as extensibility and composability. X-TIME defines a set of meta types which correspond to g meta level semantic descriptors of data models found in the Web. The meta types are organized in a Generalization Hierarchy to capture semantic similarities among modeling concepts of interoperable systems. We show how to use the X-TIME methodology to build cooperative environments for B2B platforms involving the integration of Web data and services.

  • XML Integration and Toolkit for B2B Applications
    Journal of Database Management, 2003
    Co-Authors: Christophe Nicolle, Kokou Yétongnon, Jean-claude Simon
    Abstract:

    This paper presents a Web-based data integration methodology and tool framework, called X-TIME, for the development of business-to-business (B2B) design environments and applications. X-TIME provides a data model translator toolkit based on an extensible metamodel and XML. It allows the creation of adaptable semantics oriented metamodels to facilitate the design of wrappers or reconciliators (mediators) by taking into account several characteristics of interoperable information systems such as extensibility and composability. X-TIME defines a set of meta-types for representing meta-level semantic descriptors of data models found in the Web. The meta-types are organized in a Generalization Hierarchy to capture semantic similarities among modeling concepts of interoperable systems. We show how to use the X-TIME methodology to build cooperative environments for B2B platforms involving the integration of Web data and services.

  • CAiSE - Multi-Data Models Translations in Interoperable Information Systems
    Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 1996
    Co-Authors: Christophe Nicolle, Djamal Benslimane, Kokou Yétongnon
    Abstract:

    Interoperation of heterogeneous and autonomous information systems has traditionally been hampered by semantic differences in their data models. In this paper, we address the problem by defining a methodology called TIME, which is based on an extensible meta model. Its key features are: a set of meta-types which can be used to represent the syntax and the semantics of data modeling concepts, a knowledge base of transformation rules that map a meta-type into other meta-types, and an inference engine which uses the transformation rules to translate schema from source to target models. The extensibility of the meta-model is achieved by organizing the meta-types into a Generalization Hierarchy that record similarities among modeling concepts. The Hierarchy of meta-types allows the reuse of transformation rules during automatic generation of data model translators.

  • IDEAS - SHB: a strategic Hierarchy builder for managing heterogeneous databases
    Proceedings. IDEAS'99. International Database Engineering and Applications Symposium (Cat. No.PR00265), 1
    Co-Authors: Christophe Nicolle, N. Culllot, Kokou Yétongnon
    Abstract:

    The paper presents a methodology based on data model translation for the management of interoperable information systems. The key feature of this solution are: 1) a set of abstract metatypes that capture the characteristics of various modeling concepts; and 2) the organization of the metatypes in a Generalization Hierarchy to allow unification and correlation of data models. Description logic formalism is used to provide formal specification of the metatypes and a tool called "Strategic Hierarchy Builder" is defined to build the metatype Hierarchy semi-automatically. The Strategic Hierarchy Builder organizes the Hierarchy and implements an extensible metamodel in which new metatypes are created by specialization of existing metatypes.

  • XML-based toolkit for interoperability of web information systems
    Web-Enabled Systems Integration, 1
    Co-Authors: Christophe Nicolle, Kokou Yétongnon
    Abstract:

    This chapter presents a methodology and tools framework, called X-TIME, to support data integration via web sites. It is a data model translator toolkit based on a metamodel and XML technology that is aimed at facilitating the design of wrappers, semantic reconciliators or mediators. X-TIME is an adaptable semantics-oriented metamodel approach that takes into account important characteristics of interoperable information systems, including extensibility and composability. Extensibility requires a translation scheme that can easily integrate new data models while composability, which is the ability of an application to define dynamically the subset of data sources it needs, requires on-demand translation among a subset of data models. To meet these requirements, X-TIME is based on an extensible metamodel that defines a set of metatypes for representing meta-level semantic descriptors of data models that can be found in web sites. The metatypes are organized in a Generalization Hierarchy to capture semantic similarities between modeling concepts and correlate constituent data models of interoperable systems.

Shinsaku Kiyomoto - One of the best experts on this subject based on the ideXlab platform.

  • towards practical k anonymization correlation based construction of Generalization Hierarchy
    International Conference on Security and Cryptography, 2016
    Co-Authors: Tomoaki Mimoto, Anirban Basu, Shinsaku Kiyomoto
    Abstract:

    The privacy of individuals included in the datasets must be preserved when sensitive datasets are published. Anonymization algorithms such as k-anonymization have been proposed in order to reduce the risk of individuals in the dataset being identified. k-anonymization is the most common technique of modifying attribute values in a dataset until at least k identical records are generated. There are many algorithms that can be used to achieve k-anonymity. However, existing algorithms have the problem of information loss due to a tradeoff between data quality and anonymity. In this paper, we propose a novel method of constructing a Generalization Hierarchy for k anonymization algorithms. Our method analyses the correlation between attributes and generates an optimal Hierarchy according to the correlation. The effect of the proposed scheme has been verified using the actual data: the average of k of the datasets is 83:14, and it is around 1=3 of the value obtained by conventional methods.

  • SECRYPT - Towards Practical k-Anonymization: Correlation-based Construction of Generalization Hierarchy
    Proceedings of the 13th International Joint Conference on e-Business and Telecommunications, 2016
    Co-Authors: Tomoaki Mimoto, Anirban Basu, Shinsaku Kiyomoto
    Abstract:

    The privacy of individuals included in the datasets must be preserved when sensitive datasets are published. Anonymization algorithms such as k-anonymization have been proposed in order to reduce the risk of individuals in the dataset being identified. k-anonymization is the most common technique of modifying attribute values in a dataset until at least k identical records are generated. There are many algorithms that can be used to achieve k-anonymity. However, existing algorithms have the problem of information loss due to a tradeoff between data quality and anonymity. In this paper, we propose a novel method of constructing a Generalization Hierarchy for k anonymization algorithms. Our method analyses the correlation between attributes and generates an optimal Hierarchy according to the correlation. The effect of the proposed scheme has been verified using the actual data: the average of k of the datasets is 83:14, and it is around 1=3 of the value obtained by conventional methods.

Waranya Mahanan - One of the best experts on this subject based on the ideXlab platform.

  • Data anonymization: a novel optimal k-anonymity algorithm for identical Generalization Hierarchy data in IoT
    Service Oriented Computing and Applications, 2020
    Co-Authors: Waranya Mahanan, W. Art Chaovalitwongse, Juggapong Natwichai
    Abstract:

    Advancement in the Internet of Things (IoT) technologies makes life more convenient for people. Data sensed from the devices can be used for analyzing and responding to people’s needs seamlessly. An important consequence of such convenience is that privacy protection becomes a very important issue to be addressed effectively. Various data anonymization model has been proposed for such issue—one of the most widely applied models is the k -anonymity. The k -anonymity prevents the re-identification by replacing the input data with its more general form for transforming the data to have at least k identical tuples. In this paper, we focus on a special case of the input datasets which all the quasi-identifiers, the linkable attributes in the dataset, have identical data types, so-called identical Generalization Hierarchy (IGH). The solutions for such case will be applicable effectively to address the general IoT data privacy protection due to its data nature. We proposed a novel method to provide a globally optimized k -anonymity solution for the IGH datasets. The proposed algorithms determine an optimal solution based on the characteristics of the IGH data by visiting and evaluating only essential nodes of Generalization lattice that satisfy the k -anonymity. Since the k -anonymization problem is an NP-hard, we show that our algorithm can efficiently find an optimal k -anonymity solutions with exploiting such special characteristics of the IGH data, i.e., the optimality between the nodes in different levels of Generalization lattice. From the experimental results, it is obvious that our algorithm is much more efficient than the comparative algorithms by less searching on the given lattice.

  • NBiS - Characterizations of Local Recoding Method on k-Anonymity
    Advances in Network-Based Information Systems, 2018
    Co-Authors: Waranya Mahanan, Juggapong Natwichai, W. Art Chaovalitwongse
    Abstract:

    k-Anonymity is one of the most widely used techniques for protecting the privacy of the publishing datasets by making each individual not distinguished from at least k-1 other individuals. The local recoding method is an approach to achieve k-anonymization through suppression and Generalization. The method generalizes the dataset at the cell level. Therefore, the local recoding could achieve the k-anonymization with only a small distortion. As the optimal k-anonymity has been proved as the NP-hard problem, the plenty of optimal algorithm local recoding has been proposed. In this research, we study the characteristics of the local recoding method. In addition, we discover the special characteristic dataset that all Generalization hierarchies of each quasi-identifier are identical, called an “Identical Generalization Hierarchy” (IGH) data. We also compare the efficiency of the well-known algorithms of the local recoding method on both \(non-IGH\) and IGH data.

Christophe Nicolle - One of the best experts on this subject based on the ideXlab platform.

  • Interoperability of B2B Applications: Methods and Tools
    Advances in Database Research, 2005
    Co-Authors: Christophe Nicolle, Kokou Yétongnon, Jean-claude Simon
    Abstract:

    This paper presents a Web-based data integration methodology and tool framework, called X-TIME, for the development of Business-to-Business (B2B) design environments and applications. X-TIME provides a data model translator toolkit based on an extensible meta model and XML. It allows the creation of adaptable semantic-oriented meta models to support the design of wrappers or reconciliators (mediators), taking into account characteristics of interoperable information systems, such as extensibility and composability. X-TIME defines a set of meta types which correspond to g meta level semantic descriptors of data models found in the Web. The meta types are organized in a Generalization Hierarchy to capture semantic similarities among modeling concepts of interoperable systems. We show how to use the X-TIME methodology to build cooperative environments for B2B platforms involving the integration of Web data and services.

  • XML Integration and Toolkit for B2B Applications
    Journal of Database Management, 2003
    Co-Authors: Christophe Nicolle, Kokou Yétongnon, Jean-claude Simon
    Abstract:

    This paper presents a Web-based data integration methodology and tool framework, called X-TIME, for the development of business-to-business (B2B) design environments and applications. X-TIME provides a data model translator toolkit based on an extensible metamodel and XML. It allows the creation of adaptable semantics oriented metamodels to facilitate the design of wrappers or reconciliators (mediators) by taking into account several characteristics of interoperable information systems such as extensibility and composability. X-TIME defines a set of meta-types for representing meta-level semantic descriptors of data models found in the Web. The meta-types are organized in a Generalization Hierarchy to capture semantic similarities among modeling concepts of interoperable systems. We show how to use the X-TIME methodology to build cooperative environments for B2B platforms involving the integration of Web data and services.

  • CAiSE - Multi-Data Models Translations in Interoperable Information Systems
    Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 1996
    Co-Authors: Christophe Nicolle, Djamal Benslimane, Kokou Yétongnon
    Abstract:

    Interoperation of heterogeneous and autonomous information systems has traditionally been hampered by semantic differences in their data models. In this paper, we address the problem by defining a methodology called TIME, which is based on an extensible meta model. Its key features are: a set of meta-types which can be used to represent the syntax and the semantics of data modeling concepts, a knowledge base of transformation rules that map a meta-type into other meta-types, and an inference engine which uses the transformation rules to translate schema from source to target models. The extensibility of the meta-model is achieved by organizing the meta-types into a Generalization Hierarchy that record similarities among modeling concepts. The Hierarchy of meta-types allows the reuse of transformation rules during automatic generation of data model translators.

  • IDEAS - SHB: a strategic Hierarchy builder for managing heterogeneous databases
    Proceedings. IDEAS'99. International Database Engineering and Applications Symposium (Cat. No.PR00265), 1
    Co-Authors: Christophe Nicolle, N. Culllot, Kokou Yétongnon
    Abstract:

    The paper presents a methodology based on data model translation for the management of interoperable information systems. The key feature of this solution are: 1) a set of abstract metatypes that capture the characteristics of various modeling concepts; and 2) the organization of the metatypes in a Generalization Hierarchy to allow unification and correlation of data models. Description logic formalism is used to provide formal specification of the metatypes and a tool called "Strategic Hierarchy Builder" is defined to build the metatype Hierarchy semi-automatically. The Strategic Hierarchy Builder organizes the Hierarchy and implements an extensible metamodel in which new metatypes are created by specialization of existing metatypes.

  • XML-based toolkit for interoperability of web information systems
    Web-Enabled Systems Integration, 1
    Co-Authors: Christophe Nicolle, Kokou Yétongnon
    Abstract:

    This chapter presents a methodology and tools framework, called X-TIME, to support data integration via web sites. It is a data model translator toolkit based on a metamodel and XML technology that is aimed at facilitating the design of wrappers, semantic reconciliators or mediators. X-TIME is an adaptable semantics-oriented metamodel approach that takes into account important characteristics of interoperable information systems, including extensibility and composability. Extensibility requires a translation scheme that can easily integrate new data models while composability, which is the ability of an application to define dynamically the subset of data sources it needs, requires on-demand translation among a subset of data models. To meet these requirements, X-TIME is based on an extensible metamodel that defines a set of metatypes for representing meta-level semantic descriptors of data models that can be found in web sites. The metatypes are organized in a Generalization Hierarchy to capture semantic similarities between modeling concepts and correlate constituent data models of interoperable systems.