The Experts below are selected from a list of 33987 Experts worldwide ranked by ideXlab platform

Jaideep Vaidya - One of the best experts on this subject based on the ideXlab platform.

  • Privacy-preserving item-based recommendations over Partitioned Data with overlaps
    International Journal of Business Information Systems, 2017
    Co-Authors: Ibrahim Yakut, Jaideep Vaidya
    Abstract:

    User ratings are vital elements to drive recommender systems and, in the case of an insufficient amount of ratings, companies may prefer to operate recommender services over Partitioned Data. To make this feasible, there are privacy-preserving schemes. However, such solutions currently have not comprehensively investigated probable rating overlaps among Partitioned Data. Such overlaps make collaboration over Partitioned Data more challenging, especially if overlapped values are divergent. In this study, we examine this privacy-preserving recommender problem and propose novel schemes in this sense. By means of our schemes, two parties can perform item-based collaborative filtering over Partitioned Data with divergent overlaps. We also show that the proposed solutions promote prediction quality with tolerable overheads.

  • Privacy-Preserving Kth Element Score over Vertically Partitioned Data
    IEEE Transactions on Knowledge and Data Engineering, 2009
    Co-Authors: Jaideep Vaidya, Chris Clifton
    Abstract:

    Given a large integer Data set shared vertically by two parties, we consider the problem of securely computing a score separating the kth and the (k + 1) to compute such a score while revealing little additional information. The proposed protocol is implemented using the Fairplay system and experimental results are reported. We show a real application of this protocol as a component used in the secure processing of top-k queries over vertically Partitioned Data.

  • Privacy-preserving decision trees over vertically Partitioned Data
    ACM Transactions on Knowledge Discovery from Data, 2008
    Co-Authors: Jaideep Vaidya, Chris Clifton, Murat Kantarcioglu, A. Scott Patterson
    Abstract:

    Privacy and security concerns can prevent sharing of Data, derailing Data-mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce a generalized privacy-preserving variant of the ID3 algorithm for vertically Partitioned Data distributed over two or more parties. Along with a proof of security, we discuss what would be necessary to make the protocols completely secure. We also provide experimental results, giving a first demonstration of the practical complexity of secure multiparty computation-based Data mining.

  • Privacy-preserving SVM classification on vertically Partitioned Data
    Lecture Notes in Computer Science, 2006
    Co-Authors: Jaideep Vaidya, Xiaoqian Jiang
    Abstract:

    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.

  • PAKDD - Privacy-Preserving SVM classification on vertically Partitioned Data
    Advances in Knowledge Discovery and Data Mining, 2006
    Co-Authors: Jaideep Vaidya, Xiaoqian Jiang
    Abstract:

    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.

Willy Zwaenepoel - One of the best experts on this subject based on the ideXlab platform.

  • clock si snapshot isolation for Partitioned Data stores using loosely synchronized clocks
    Symposium on Reliable Distributed Systems, 2013
    Co-Authors: Sameh Elnikety, Willy Zwaenepoel
    Abstract:

    Clock-SI is a fully distributed protocol that implements snapshot isolation (SI) for Partitioned Data stores. It derives snapshot and commit timestamps from loosely synchronized clocks, rather than from a centralized timestamp authority as used in current systems. A transaction obtains its snapshot timestamp by reading the clock at its originating partition and Clock-SI provides the corresponding consistent snapshot across all the partitions. In contrast to using a centralized timestamp authority, Clock-SI has availability and performance benefits: It avoids a single point of failure and a potential performance bottleneck, and improves transaction latency and throughput. We develop an analytical model to study the trade-offs introduced by Clock-SI among snapshot age, delay probabilities of transactions, and abort rates of update transactions. We verify the model predictions using a system implementation. Furthermore, we demonstrate the performance benefits of Clock-SI experimentally using a micro-benchmark and an application-level benchmark on a Partitioned key-value store. For short read-only transactions, Clock-SI improves latency and throughput by 50% by avoiding communications with a centralized timestamp authority. With a geographically Partitioned Data store, Clock-SI reduces transaction latency by more than 100 milliseconds. Moreover, the performance benefits of Clock-SI come with higher availability.

  • SRDS - Clock-SI: Snapshot Isolation for Partitioned Data Stores Using Loosely Synchronized Clocks
    2013 IEEE 32nd International Symposium on Reliable Distributed Systems, 2013
    Co-Authors: Sameh Elnikety, Willy Zwaenepoel
    Abstract:

    Clock-SI is a fully distributed protocol that implements snapshot isolation (SI) for Partitioned Data stores. It derives snapshot and commit timestamps from loosely synchronized clocks, rather than from a centralized timestamp authority as used in current systems. A transaction obtains its snapshot timestamp by reading the clock at its originating partition and Clock-SI provides the corresponding consistent snapshot across all the partitions. In contrast to using a centralized timestamp authority, Clock-SI has availability and performance benefits: It avoids a single point of failure and a potential performance bottleneck, and improves transaction latency and throughput. We develop an analytical model to study the trade-offs introduced by Clock-SI among snapshot age, delay probabilities of transactions, and abort rates of update transactions. We verify the model predictions using a system implementation. Furthermore, we demonstrate the performance benefits of Clock-SI experimentally using a micro-benchmark and an application-level benchmark on a Partitioned key-value store. For short read-only transactions, Clock-SI improves latency and throughput by 50% by avoiding communications with a centralized timestamp authority. With a geographically Partitioned Data store, Clock-SI reduces transaction latency by more than 100 milliseconds. Moreover, the performance benefits of Clock-SI come with higher availability.

Yücel Saygın - One of the best experts on this subject based on the ideXlab platform.

  • Ubiquitous Knowledge Discovery - Privacy preserving spatio-temporal clustering on horizontally Partitioned Data
    2010
    Co-Authors: Ali İnan, Yücel Saygın
    Abstract:

    Space and time are two important features of Data collected in ubiquitous environments. Such time-stamped location information is regarded as spatio-temporal Data and, by its nature, spatio-temporal Data sets, when they describe the movement behavior of individuals, are highly privacy sensitive. In this chapter, we propose a privacy preserving spatio-temporal clustering method for horizontally Partitioned Data. Our methods are based on building the dissimilarity matrix through a series of secure multi-party trajectory comparisons managed by a third party. Our trajectory comparison protocol complies with most trajectory comparison functions. A complexity analysis of our methods shows that our protocol does not introduce extra overhead when constructing dissimilarity matrices, compared to the centralized approach.

  • privacy preserving clustering on horizontally Partitioned Data
    Data and Knowledge Engineering, 2007
    Co-Authors: Ali İnan, Yücel Saygın, Selim Volkan Kaya, Erkay Savas, Ayca Azgin Hintoglu, Albert Levi
    Abstract:

    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.

  • Privacy Preserving Spatio-Temporal Clustering on Horizontally Partitioned Data
    Data Warehousing and Knowledge Discovery, 2006
    Co-Authors: Ali İnan, Yücel Saygın
    Abstract:

    Time-stamped location information is regarded as spatio-temporal Data and, by its nature, such Data is highly sensitive from the perspective of privacy. In this paper, we propose a privacy preserving spatio-temporal clustering method for horizontally Partitioned Data which, to the best of our knowledge, was not done before. Our methods are based on building the dissimilarity matrix through a series of secure multi-party trajectory comparisons managed by a third party. Our trajectory comparison protocol complies with most trajectory comparison functions and complexity analysis of our methods shows that our protocol does not introduce extra overhead when constructing dissimilarity matrix, compared to the centralized approach. This work was funded by the Information Society Technologies programme of the European Commission, Future and Emerging Technologies under IST-014915 GeoPKDD project

Chris Clifton - One of the best experts on this subject based on the ideXlab platform.

  • Privacy-Preserving Kth Element Score over Vertically Partitioned Data
    IEEE Transactions on Knowledge and Data Engineering, 2009
    Co-Authors: Jaideep Vaidya, Chris Clifton
    Abstract:

    Given a large integer Data set shared vertically by two parties, we consider the problem of securely computing a score separating the kth and the (k + 1) to compute such a score while revealing little additional information. The proposed protocol is implemented using the Fairplay system and experimental results are reported. We show a real application of this protocol as a component used in the secure processing of top-k queries over vertically Partitioned Data.

  • Privacy-preserving decision trees over vertically Partitioned Data
    ACM Transactions on Knowledge Discovery from Data, 2008
    Co-Authors: Jaideep Vaidya, Chris Clifton, Murat Kantarcioglu, A. Scott Patterson
    Abstract:

    Privacy and security concerns can prevent sharing of Data, derailing Data-mining projects. Distributed knowledge discovery, if done correctly, can alleviate this problem. We introduce a generalized privacy-preserving variant of the ID3 algorithm for vertically Partitioned Data distributed over two or more parties. Along with a proof of security, we discuss what would be necessary to make the protocols completely secure. We also provide experimental results, giving a first demonstration of the practical complexity of secure multiparty computation-based Data mining.

  • privacy preserving decision trees over vertically Partitioned Data
    Lecture Notes in Computer Science, 2005
    Co-Authors: Jaideep Vaidya, Chris Clifton
    Abstract:

    Privacy and security concerns can prevent sharing of Data, derailing Data mining projects.Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically Partitioned Data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.

  • DBSec - Privacy-Preserving decision trees over vertically Partitioned Data
    Data and Applications Security XIX, 2005
    Co-Authors: Jaideep Vaidya, Chris Clifton
    Abstract:

    Privacy and security concerns can prevent sharing of Data, derailing Data mining projects.Distributed knowledge discovery, if done correctly, can alleviate this problem. In this paper, we tackle the problem of classification. We introduce a generalized privacy preserving variant of the ID3 algorithm for vertically Partitioned Data distributed over two or more parties. Along with the algorithm, we give a complete proof of security that gives a tight bound on the information revealed.

  • Privacy-preserving distributed Data mining and processing on horizontally Partitioned Data
    2005
    Co-Authors: Murat Kantarcioglu, Chris Clifton
    Abstract:

    Data mining can extract important knowledge from large Data collections, but sometimes these collections are split among various parties. Data warehousing, bringing Data from multiple sources under a single authority, increases risk of privacy violations. Furthermore, privacy concerns may prevent the parties from directly sharing even some meta-Data. Distributed Data mining and processing provide a means to address this issue, particularly if queries are processed in a way that avoids the disclosure of any information beyond the final result. This thesis presents methods to mine horizontally Partitioned Data without violating privacy and shows how to use the Data mining results in a privacy-preserving way. The methods incorporate cryptographic techniques to minimize the information shared, while adding as little as possible overhead to the mining and processing task.

Ali İnan - One of the best experts on this subject based on the ideXlab platform.

  • Ubiquitous Knowledge Discovery - Privacy preserving spatio-temporal clustering on horizontally Partitioned Data
    2010
    Co-Authors: Ali İnan, Yücel Saygın
    Abstract:

    Space and time are two important features of Data collected in ubiquitous environments. Such time-stamped location information is regarded as spatio-temporal Data and, by its nature, spatio-temporal Data sets, when they describe the movement behavior of individuals, are highly privacy sensitive. In this chapter, we propose a privacy preserving spatio-temporal clustering method for horizontally Partitioned Data. Our methods are based on building the dissimilarity matrix through a series of secure multi-party trajectory comparisons managed by a third party. Our trajectory comparison protocol complies with most trajectory comparison functions. A complexity analysis of our methods shows that our protocol does not introduce extra overhead when constructing dissimilarity matrices, compared to the centralized approach.

  • privacy preserving clustering on horizontally Partitioned Data
    Data and Knowledge Engineering, 2007
    Co-Authors: Ali İnan, Yücel Saygın, Selim Volkan Kaya, Erkay Savas, Ayca Azgin Hintoglu, Albert Levi
    Abstract:

    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.

  • Privacy Preserving Spatio-Temporal Clustering on Horizontally Partitioned Data
    Data Warehousing and Knowledge Discovery, 2006
    Co-Authors: Ali İnan, Yücel Saygın
    Abstract:

    Time-stamped location information is regarded as spatio-temporal Data and, by its nature, such Data is highly sensitive from the perspective of privacy. In this paper, we propose a privacy preserving spatio-temporal clustering method for horizontally Partitioned Data which, to the best of our knowledge, was not done before. Our methods are based on building the dissimilarity matrix through a series of secure multi-party trajectory comparisons managed by a third party. Our trajectory comparison protocol complies with most trajectory comparison functions and complexity analysis of our methods shows that our protocol does not introduce extra overhead when constructing dissimilarity matrix, compared to the centralized approach. This work was funded by the Information Society Technologies programme of the European Commission, Future and Emerging Technologies under IST-014915 GeoPKDD project