The Experts below are selected from a list of 225 Experts worldwide ranked by ideXlab platform
Kyuseok Shim - One of the best experts on this subject based on the ideXlab platform.
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ICDE - TIDY: Publishing a Time Interval Dataset with Differential Privacy (Extended abstract)
2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020Co-Authors: Woohwan Jung, Suyong Kwon, Kyuseok ShimAbstract:Log data from mobile devices usually contain a series of events with time intervals. However, the problem of releasing differentially private time interval data has not been tackled yet. We propose the TIDY (publishing Time Intervals via Differential Privacy) Algorithm to release time interval data under differential Privacy. We use the frequency vectors as a compact representation of the time interval data to reduce the aggregated noise. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our experiments confirm that TIDY outperforms the existing Algorithms for releasing 2D histograms.
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TIDY: Publishing a Time Interval Dataset with Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 1Co-Authors: Woohwan Jung, Suyong Kwon, Kyuseok ShimAbstract:Log data from mobile devices generally contain a series of events with temporal information including time intervals which consist of the start and finish times. However, the problem of releasing differentially private time interval datasets has not been tackled yet. A time interval dataset can be represented by a two dimensional (2D) histogram. Most of the methods to publish 2D histograms partition the data into rectangular spaces to reduce the aggregated noise error for range queries. However, the existing Algorithms to publish 2D histograms suffer from the structural error when applied to time interval datasets. To reduce the aggregated noise errors and suppress the increase in the structural error, we propose the TIDY (publishing Time Intervals via Differential Privacy) Algorithm. We use the frequency vectors as a compact representation of the time interval dataset. After applying the Laplace mechanism to the frequency vectors, we improve the utility of the frequency vectors based on a maximum likelihood estimation. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our empirical study on real-life and synthetic datasets confirms that TIDY outperforms the existing Algorithms for 2D histograms.
Woohwan Jung - One of the best experts on this subject based on the ideXlab platform.
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ICDE - TIDY: Publishing a Time Interval Dataset with Differential Privacy (Extended abstract)
2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020Co-Authors: Woohwan Jung, Suyong Kwon, Kyuseok ShimAbstract:Log data from mobile devices usually contain a series of events with time intervals. However, the problem of releasing differentially private time interval data has not been tackled yet. We propose the TIDY (publishing Time Intervals via Differential Privacy) Algorithm to release time interval data under differential Privacy. We use the frequency vectors as a compact representation of the time interval data to reduce the aggregated noise. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our experiments confirm that TIDY outperforms the existing Algorithms for releasing 2D histograms.
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TIDY: Publishing a Time Interval Dataset with Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 1Co-Authors: Woohwan Jung, Suyong Kwon, Kyuseok ShimAbstract:Log data from mobile devices generally contain a series of events with temporal information including time intervals which consist of the start and finish times. However, the problem of releasing differentially private time interval datasets has not been tackled yet. A time interval dataset can be represented by a two dimensional (2D) histogram. Most of the methods to publish 2D histograms partition the data into rectangular spaces to reduce the aggregated noise error for range queries. However, the existing Algorithms to publish 2D histograms suffer from the structural error when applied to time interval datasets. To reduce the aggregated noise errors and suppress the increase in the structural error, we propose the TIDY (publishing Time Intervals via Differential Privacy) Algorithm. We use the frequency vectors as a compact representation of the time interval dataset. After applying the Laplace mechanism to the frequency vectors, we improve the utility of the frequency vectors based on a maximum likelihood estimation. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our empirical study on real-life and synthetic datasets confirms that TIDY outperforms the existing Algorithms for 2D histograms.
Suyong Kwon - One of the best experts on this subject based on the ideXlab platform.
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ICDE - TIDY: Publishing a Time Interval Dataset with Differential Privacy (Extended abstract)
2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020Co-Authors: Woohwan Jung, Suyong Kwon, Kyuseok ShimAbstract:Log data from mobile devices usually contain a series of events with time intervals. However, the problem of releasing differentially private time interval data has not been tackled yet. We propose the TIDY (publishing Time Intervals via Differential Privacy) Algorithm to release time interval data under differential Privacy. We use the frequency vectors as a compact representation of the time interval data to reduce the aggregated noise. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our experiments confirm that TIDY outperforms the existing Algorithms for releasing 2D histograms.
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TIDY: Publishing a Time Interval Dataset with Differential Privacy
IEEE Transactions on Knowledge and Data Engineering, 1Co-Authors: Woohwan Jung, Suyong Kwon, Kyuseok ShimAbstract:Log data from mobile devices generally contain a series of events with temporal information including time intervals which consist of the start and finish times. However, the problem of releasing differentially private time interval datasets has not been tackled yet. A time interval dataset can be represented by a two dimensional (2D) histogram. Most of the methods to publish 2D histograms partition the data into rectangular spaces to reduce the aggregated noise error for range queries. However, the existing Algorithms to publish 2D histograms suffer from the structural error when applied to time interval datasets. To reduce the aggregated noise errors and suppress the increase in the structural error, we propose the TIDY (publishing Time Intervals via Differential Privacy) Algorithm. We use the frequency vectors as a compact representation of the time interval dataset. After applying the Laplace mechanism to the frequency vectors, we improve the utility of the frequency vectors based on a maximum likelihood estimation. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our empirical study on real-life and synthetic datasets confirms that TIDY outperforms the existing Algorithms for 2D histograms.
Marco Gruteser - One of the best experts on this subject based on the ideXlab platform.
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protecting location Privacy through path confusion
International Workshop on Security, 2005Co-Authors: Baik Hoh, Marco GruteserAbstract:We present a path perturbation Algorithm which can maximize users location Privacy given a quality of service constraint. This work concentrates on a class of applications that continuously collect location samples from a large group of users, where just removing user identifiers from all samples is insufficient because an adversary could use trajectory information to track paths and follow users footsteps home. The key idea underlying the perturbation Algorithm is to cross paths in areas where at least two users meet. This increases the chances that an adversary would confuse the paths of different users. We first formulate this Privacy problem as a constrained optimization problem and then develop heuristics for an efficient Privacy Algorithm. Using simulations with randomized movement models we verify that the Algorithm improves Privacy while minimizing the perturbation of location samples.
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SecureComm - Protecting Location Privacy Through Path Confusion
First International Conference on Security and Privacy for Emerging Areas in Communications Networks (SECURECOMM'05), 1Co-Authors: Baik Hoh, Marco GruteserAbstract:We present a path perturbation Algorithm which can maximize users location Privacy given a quality of service constraint. This work concentrates on a class of applications that continuously collect location samples from a large group of users, where just removing user identifiers from all samples is insufficient because an adversary could use trajectory information to track paths and follow users footsteps home. The key idea underlying the perturbation Algorithm is to cross paths in areas where at least two users meet. This increases the chances that an adversary would confuse the paths of different users. We first formulate this Privacy problem as a constrained optimization problem and then develop heuristics for an efficient Privacy Algorithm. Using simulations with randomized movement models we verify that the Algorithm improves Privacy while minimizing the perturbation of location samples.
Bingqing Zhu - One of the best experts on this subject based on the ideXlab platform.
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ICCS (2) - A Workload Division Differential Privacy Algorithm to Improve the Accuracy for Linear Computations.
Lecture Notes in Computer Science, 2020Co-Authors: Yanqin Zhang, Zhen Hui, Lei Zhang, Bingqing ZhuAbstract:Differential Privacy Algorithm is an effective technology to protect data Privacy, and there are many pieces of research about differential Privacy and some practical applications from the Internet companies, such as Apple and Google, etc. By differential Privacy technology, the data organizations can allow external data scientists to explore their sensitive datasets, and the data owners can be ensured provable Privacy guarantees meanwhile. It is inevitable that the query results that will cause the error, as a consequence that the differential Privacy Algorithm would disturb the data, and some differential Privacy Algorithms are aimed to reduce the introduced noise. However, those Algorithms just adopt to the simple or relative uniform data, when the data distribution is complex, some Algorithms will lose efficiency. In this paper, we propose a new simple \(\varepsilon \)-differential Privacy Algorithm. Our approach includes two key points: Firstly, we used Laplace-based noise to disturb answer to reduce the error of the linear computation queries under intensive data items by workload-aware noise; Secondly, we propose an optimized workload division method. We divide the queries recursively to reduce the added noise, which can reduce computation error when there exists query hot spot in the workload. We conduct extensive evaluation over six real-world datasets to examine the performance of our approach. The experimental results show that our approach can reduce nearly 40% computation error for linear computation when compared with MWEM, DAWA, and Identity. Meanwhile, our approach can achieve better response time to answer the query cases compared with the start-of-the-art Algorithms.
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a workload division differential Privacy Algorithm to improve the accuracy for linear computations
International Conference on Computational Science, 2020Co-Authors: Yanqin Zhang, Zhen Hui, Lei Zhang, Bingqing ZhuAbstract:Differential Privacy Algorithm is an effective technology to protect data Privacy, and there are many pieces of research about differential Privacy and some practical applications from the Internet companies, such as Apple and Google, etc. By differential Privacy technology, the data organizations can allow external data scientists to explore their sensitive datasets, and the data owners can be ensured provable Privacy guarantees meanwhile. It is inevitable that the query results that will cause the error, as a consequence that the differential Privacy Algorithm would disturb the data, and some differential Privacy Algorithms are aimed to reduce the introduced noise. However, those Algorithms just adopt to the simple or relative uniform data, when the data distribution is complex, some Algorithms will lose efficiency. In this paper, we propose a new simple \(\varepsilon \)-differential Privacy Algorithm. Our approach includes two key points: Firstly, we used Laplace-based noise to disturb answer to reduce the error of the linear computation queries under intensive data items by workload-aware noise; Secondly, we propose an optimized workload division method. We divide the queries recursively to reduce the added noise, which can reduce computation error when there exists query hot spot in the workload. We conduct extensive evaluation over six real-world datasets to examine the performance of our approach. The experimental results show that our approach can reduce nearly 40% computation error for linear computation when compared with MWEM, DAWA, and Identity. Meanwhile, our approach can achieve better response time to answer the query cases compared with the start-of-the-art Algorithms.