The Experts below are selected from a list of 5721 Experts worldwide ranked by ideXlab platform
Xiaoqian Jiang - One of the best experts on this subject based on the ideXlab platform.
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Privacy-preserving SVM classification on vertically partitioned data
Lecture Notes in Computer Science, 2006Co-Authors: Jaideep Vaidya, Xiaoqian JiangAbstract:Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharing of data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do distributed knowledge discovery, while providing guarantees on the non-disclosure of data. Classification is an important data mining problem applicable in many diverse domains. The goal of classification is to build a model which can predict an Attribute (Binary Attribute in this work) based on the rest of Attributes. We propose an efficient and secure privacy-preserving algorithm for support vector machine (SVM) classification over vertically partitioned data.
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PAKDD - Privacy-Preserving SVM classification on vertically partitioned data
Advances in Knowledge Discovery and Data Mining, 2006Co-Authors: Jaideep Vaidya, Xiaoqian JiangAbstract:Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharing of data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do distributed knowledge discovery, while providing guarantees on the non-disclosure of data. Classification is an important data mining problem applicable in many diverse domains. The goal of classification is to build a model which can predict an Attribute (Binary Attribute in this work) based on the rest of Attributes. We propose an efficient and secure privacy-preserving algorithm for support vector machine (SVM) classification over vertically partitioned data.
Jaideep Vaidya - One of the best experts on this subject based on the ideXlab platform.
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Privacy-preserving SVM classification on vertically partitioned data
Lecture Notes in Computer Science, 2006Co-Authors: Jaideep Vaidya, Xiaoqian JiangAbstract:Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharing of data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do distributed knowledge discovery, while providing guarantees on the non-disclosure of data. Classification is an important data mining problem applicable in many diverse domains. The goal of classification is to build a model which can predict an Attribute (Binary Attribute in this work) based on the rest of Attributes. We propose an efficient and secure privacy-preserving algorithm for support vector machine (SVM) classification over vertically partitioned data.
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PAKDD - Privacy-Preserving SVM classification on vertically partitioned data
Advances in Knowledge Discovery and Data Mining, 2006Co-Authors: Jaideep Vaidya, Xiaoqian JiangAbstract:Classical data mining algorithms implicitly assume complete access to all data, either in centralized or federated form. However, privacy and security concerns often prevent sharing of data, thus derailing data mining projects. Recently, there has been growing focus on finding solutions to this problem. Several algorithms have been proposed that do distributed knowledge discovery, while providing guarantees on the non-disclosure of data. Classification is an important data mining problem applicable in many diverse domains. The goal of classification is to build a model which can predict an Attribute (Binary Attribute in this work) based on the rest of Attributes. We propose an efficient and secure privacy-preserving algorithm for support vector machine (SVM) classification over vertically partitioned data.
Arjan Kuijper - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised privacy-enhancement of face representations using similarity-sensitive noise transformations
Applied Intelligence, 2019Co-Authors: Philipp Terhorst, Naser Damer, Florian Kirchbuchner, Arjan KuijperAbstract:Face images processed by a biometric system are expected to be used for recognition purposes only. However, recent work presented possibilities for automatically deducing additional information about an individual from their face data. By using soft-biometric estimators, information about gender, age, ethnicity, sexual orientation or the health state of a person can be obtained. This raises a major privacy issue. Previous works presented supervised solutions that require large amount of private data in order to suppress a single Attribute. In this work, we propose a privacy-preserving solution that does not require these sensitive information and thus, works in an unsupervised manner. Further, our approach offers privacy protection that is not limited to a single known Binary Attribute or classifier. We do that by proposing similarity-sensitive noise transformations and investigate their effect and the effect of dimensionality reduction methods on the task of privacy preservation. Experiments are done on a publicly available database and contain analyses of the recognition performance, as well as investigations of the estimation performance of the Binary Attribute of gender and the continuous Attribute of age. We further investigated the estimation performance of these Attributes when the prior knowledge about the used privacy mechanism is explicitly utilized. The results show that using this information leads to significantly enhancement of the estimation quality. Finally, we proposed a metric to evaluate the trade-off between the privacy gain and the recognition loss for privacy-preservation techniques. Our experiments showed that the proposed cosine-sensitive noise transformation was successful in reducing the possibility of estimating the soft private information in the data, while having significantly smaller effect on the intended recognition performance.
Michael T Goodrich - One of the best experts on this subject based on the ideXlab platform.
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turning privacy leaks into floods surreptitious discovery of social network friendships and other sensitive Binary Attribute vectors
Workshop on Privacy in the Electronic Society, 2010Co-Authors: Arthur U Asuncion, Michael T GoodrichAbstract:We study methods for attacking the privacy of social networking sites, collaborative filtering sites, databases of genetic signatures, and other data sets that can be represented as vectors of Binary relationships. Our methods are based on reductions to nonadaptive group testing, which implies that our methods can exploit a minimal amount of privacy leakage, such as contained in a single bit that indicates if two people in a social network have a friend in common or not. We analyze our methods for turning such privacy leaks into floods using theoretical characterizations as well as experimental tests. Our empirical analyses are based on experiments involving privacy attacks on the social networking sites Facebook and LiveJournal, a database of mitochondrial DNA, a power grid network, and the movie-rating database released as a part of the Netflix Prize contest. For instance, with respect to Facebook, our analysis shows that it is effectively possible to break the privacy of members who restrict their friends lists to friends-of-friends.
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WPES - Turning privacy leaks into floods: surreptitious discovery of social network friendships and other sensitive Binary Attribute vectors
Proceedings of the 9th annual ACM workshop on Privacy in the electronic society - WPES '10, 2010Co-Authors: Arthur U Asuncion, Michael T GoodrichAbstract:We study methods for attacking the privacy of social networking sites, collaborative filtering sites, databases of genetic signatures, and other data sets that can be represented as vectors of Binary relationships. Our methods are based on reductions to nonadaptive group testing, which implies that our methods can exploit a minimal amount of privacy leakage, such as contained in a single bit that indicates if two people in a social network have a friend in common or not. We analyze our methods for turning such privacy leaks into floods using theoretical characterizations as well as experimental tests. Our empirical analyses are based on experiments involving privacy attacks on the social networking sites Facebook and LiveJournal, a database of mitochondrial DNA, a power grid network, and the movie-rating database released as a part of the Netflix Prize contest. For instance, with respect to Facebook, our analysis shows that it is effectively possible to break the privacy of members who restrict their friends lists to friends-of-friends.
Philipp Terhorst - One of the best experts on this subject based on the ideXlab platform.
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Unsupervised privacy-enhancement of face representations using similarity-sensitive noise transformations
Applied Intelligence, 2019Co-Authors: Philipp Terhorst, Naser Damer, Florian Kirchbuchner, Arjan KuijperAbstract:Face images processed by a biometric system are expected to be used for recognition purposes only. However, recent work presented possibilities for automatically deducing additional information about an individual from their face data. By using soft-biometric estimators, information about gender, age, ethnicity, sexual orientation or the health state of a person can be obtained. This raises a major privacy issue. Previous works presented supervised solutions that require large amount of private data in order to suppress a single Attribute. In this work, we propose a privacy-preserving solution that does not require these sensitive information and thus, works in an unsupervised manner. Further, our approach offers privacy protection that is not limited to a single known Binary Attribute or classifier. We do that by proposing similarity-sensitive noise transformations and investigate their effect and the effect of dimensionality reduction methods on the task of privacy preservation. Experiments are done on a publicly available database and contain analyses of the recognition performance, as well as investigations of the estimation performance of the Binary Attribute of gender and the continuous Attribute of age. We further investigated the estimation performance of these Attributes when the prior knowledge about the used privacy mechanism is explicitly utilized. The results show that using this information leads to significantly enhancement of the estimation quality. Finally, we proposed a metric to evaluate the trade-off between the privacy gain and the recognition loss for privacy-preservation techniques. Our experiments showed that the proposed cosine-sensitive noise transformation was successful in reducing the possibility of estimating the soft private information in the data, while having significantly smaller effect on the intended recognition performance.