The Experts below are selected from a list of 35214 Experts worldwide ranked by ideXlab platform
Charu C Aggarwal - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preserving data mining models and algorithms
2008Co-Authors: Charu C AggarwalAbstract:Advances in hardware technology have increased the capability to store and record personal data about consumers and individuals, causing concerns that personal data may be used for a variety of intrusive or malicious purposes. Privacy-Preserving Data Mining: Models and Algorithms proposes a number of techniques to perform the data mining tasks in a Privacy-Preserving way. These techniques generally fall into the following categories: data modification techniques, cryptographic methods and protocols for data sharing, statistical techniques for disclosure and inference control, query auditing methods, randomization and perturbation-based techniques. This edited volume contains surveys by distinguished researchers in the Privacy field. Each survey includes the key research content as well as future research directions. Privacy-Preserving Data Mining: Models and Algorithms is designed for researchers, professors, and advanced-level students in computer science, and is also suitable for industry practitioners.
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a general survey of Privacy Preserving data mining models and algorithms
Privacy-Preserving Data Mining, 2008Co-Authors: Charu C AggarwalAbstract:In recent years, Privacy-Preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for Privacy-Preserving data mining. In this paper, we provide a review of the state-of-the-art methods for Privacy. We discuss methods for randomization, k-anonymization, and distributed Privacy-Preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for Privacy-preservation purposes. We discuss the computational and theoretical limits associated with Privacy-preservation over high dimensional data sets.
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a condensation approach to Privacy Preserving data mining
Lecture Notes in Computer Science, 2004Co-Authors: Charu C AggarwalAbstract:In recent years, Privacy Preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the Privacy of sensitive information is guaranteed. In this paper, we propose a new framework for Privacy Preserving data mining of multi-dimensional data. Previous work for Privacy Preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for Privacy Preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method.
Rebecca N Wright - One of the best experts on this subject based on the ideXlab platform.
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a new Privacy Preserving distributed k clustering algorithm
SIAM International Conference on Data Mining, 2006Co-Authors: Geetha Jagannathan, Krishnan Pillaipakkamnatt, Rebecca N WrightAbstract:We present a simple I/O-efficient k-clustering algorithm that was designed with the goal of enabling a Privacy-Preserving version of the algorithm. Our experiments show that this algorithm produces cluster centers that are, on average, more accurate than the ones produced by the well known iterative k-means algorithm. We use our new algorithm as the basis for a communication-efficient Privacy-Preservingk-clustering protocol for databases that are horizontally partitioned between two parties. Unlike existing Privacy-Preserving protocols based on the k-means algorithm, this protocol does not reveal intermediate candidate cluster centers.
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experimental analysis of a Privacy Preserving scalar product protocol
Computer Systems: Science & Engineering, 2006Co-Authors: Zhiqiang Yang, Rebecca N Wright, Hiranmayee SubramaniamAbstract:The recent investigation of Privacy-Preserving data mining has been motivated by the growing concern about the Privacy of individuals when their data is stored, aggregated, and mined for information. In an effort towards practical algorithms for Privacy-Preserving data mining solutions, we analyze and implement solutions to an important primitive: the Privacy-Preserving scalar product of two vectors held by different parties. PrivacyPreserving scalar products are an important component of Privacy-Preserving data mining algorithms, particularly when data is vertically partitioned between two or more parties. We examine a cryptographically secure PrivacyPreserving data mining solution in different computational settings. Our experimental results show that in the absence of special-purpose hardware accelerators or practical optimizations, the computational complexity, rather than the communication complexity, is the performance bottleneck. We also evaluate several practical optimizations to improve the efficiency.
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Privacy Preserving distributed k means clustering over arbitrarily partitioned data
Knowledge Discovery and Data Mining, 2005Co-Authors: Geetha Jagannathan, Rebecca N WrightAbstract:Advances in computer networking and database technologies have enabled the collection and storage of vast quantities of data. Data mining can extract valuable knowledge from this data, and organizations have realized that they can often obtain better results by pooling their data together. However, the collected data may contain sensitive or private information about the organizations or their customers, and Privacy concerns are exacerbated if data is shared between multiple organizations.Distributed data mining is concerned with the computation of models from data that is distributed among multiple participants. Privacy-Preserving distributed data mining seeks to allow for the cooperative computation of such models without the cooperating parties revealing any of their individual data items. Our paper makes two contributions in Privacy-Preserving data mining. First, we introduce the concept of arbitrarily partitioned data, which is a generalization of both horizontally and vertically partitioned data. Second, we provide an efficient Privacy-Preserving protocol for k-means clustering in the setting of arbitrarily partitioned data.
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Privacy Preserving bayesian network structure computation on distributed heterogeneous data
Knowledge Discovery and Data Mining, 2004Co-Authors: Rebecca N Wright, Zhiqiang YangAbstract:As more and more activities are carried out using computers and computer networks, the amount of potentially sensitive data stored by business, governments, and other parties increases. Different parties may wish to benefit from cooperative use of their data, but Privacy regulations and other Privacy concerns may prevent the parties from sharing their data. Privacy-Preserving data mining provides a solution by creating distributed data mining algorithms in which the underlying data is not revealed.In this paper, we present a Privacy-Preserving protocol for a particular data mining task: learning the Bayesian network structure for distributed heterogeneous data. In this setting, two parties owning confidential databases wish to learn the structure of Bayesian network on the combination of their databases without revealing anything about their data to each other. We give an efficient and Privacy-Preserving version of the K2 algorithm to construct the structure of a Bayesian network for the parties' joint data.
Albert Levi - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preserving clustering on horizontally partitioned data
Data and Knowledge Engineering, 2007Co-Authors: Ali İnan, Yücel Saygın, Selim Volkan Kaya, Erkay Savas, Ayca Azgin Hintoglu, Albert LeviAbstract:Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the Privacy of individuals. The aim of Privacy Preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the Privacy of individuals. Privacy Preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments. In this work, we propose methods for constructing the dissimilarity matrix of objects from different sites in a Privacy Preserving manner which can be used for Privacy Preserving clustering as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects horizontally distributed to multiple sites. We show communication and computation complexity of our protocol by conducting experiments over synthetically generated and real datasets. Each experiment is also performed for a baseline protocol, which has no Privacy concern to show that the overhead comes with security and Privacy by comparing the baseline protocol and our protocol.
Ali İnan - One of the best experts on this subject based on the ideXlab platform.
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Privacy Preserving clustering on horizontally partitioned data
Data and Knowledge Engineering, 2007Co-Authors: Ali İnan, Yücel Saygın, Selim Volkan Kaya, Erkay Savas, Ayca Azgin Hintoglu, Albert LeviAbstract:Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the Privacy of individuals. The aim of Privacy Preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the Privacy of individuals. Privacy Preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments. In this work, we propose methods for constructing the dissimilarity matrix of objects from different sites in a Privacy Preserving manner which can be used for Privacy Preserving clustering as well as database joins, record linkage and other operations that require pair-wise comparison of individual private data objects horizontally distributed to multiple sites. We show communication and computation complexity of our protocol by conducting experiments over synthetically generated and real datasets. Each experiment is also performed for a baseline protocol, which has no Privacy concern to show that the overhead comes with security and Privacy by comparing the baseline protocol and our protocol.
C V Jawahar - One of the best experts on this subject based on the ideXlab platform.
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efficient Privacy Preserving video surveillance
International Conference on Computer Vision, 2009Co-Authors: Maneesh Upmanyu, Anoop M Namboodiri, Kannan Srinathan, C V JawaharAbstract:Widespread use of surveillance cameras in offices and other business establishments, pose a significant threat to the Privacy of the employees and visitors. The challenge of introducing Privacy and security in such a practical surveillance system has been stifled by the enormous computational and communication overhead required by the solutions. In this paper, we propose an efficient framework to carry out Privacy Preserving surveillance. We split each frame into a set of random images. Each image by itself does not convey any meaningful information about the original frame, while collectively, they retain all the information. Our solution is derived from a secret sharing scheme based on the Chinese Remainder Theorem, suitably adapted to image data. Our method enables distributed secure processing and storage, while retaining the ability to reconstruct the original data in case of a legal requirement. The system installed in an office like environment can effectively detect and track people, or solve similar surveillance tasks. Our proposed paradigm is highly efficient compared to Secure Multiparty Computation, making Privacy Preserving surveillance, practical.
