Differentially Private Histogram Publication

Xu, Jia; Zhang, Zhenjie; Xiao, Xiaokui; Yang, Yin; Yu, Ge

doi:10.1109/icde.2012.48

Cited by 103 publications

(55 citation statements)

References 24 publications

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“…In the process of histogram reconstruction, the intermediate result of dynamic programming algorithm M(s, t), key to obtaining the optimal structure, is the minimum error when histogram sequences x1 to xs are reconstructed to t groups [10]. The matrix form of the intermediate results of the histogram based on dynamic programming in Fig.…”

Section: Reconstruction Of Histogram and Its Errormentioning

confidence: 99%

Difference Privacy Histogram Release Based on Isotonic Regression

Shi¹,

Zhou²

2016

JSW

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Data release is likely to result in privacy disclosure, so appropriate privacy protection measures are required for various data release technologies in order to ensure the privacy and safety of information, while differential privacy as a reliable model for privacy protection is extensively researched and applied. This paper presents the histogram data publishing solutions under differential privacy model, namely adding noise on the optimized histogram structure and then carrying out isotonic regression algorithms on the histogram privacy sequence. In this case, differentcial privacy model keeps all the statistical properties of the histogram unchanged and the concealment of privacy information, and in addition, histogram reconstruction and isotonic regression algorithm are effective in improving the accuracy of data release via histogram. This paper provides a solution about isotonic regression to decrease the error on histogram reconstruction based on previous research.

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Section: Reconstruction Of Histogram and Its Errormentioning

confidence: 99%

Difference Privacy Histogram Release Based on Isotonic Regression

Shi¹,

Zhou²

2016

JSW

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“…Approximation strategies have been investigated in earlier research, such as histogram publishing [31], [32], and statistics on data stream publishing [5], [14]. Instead of directly adding noise to real data, they function by transformation of original data or a query structure to achieve better overall utility.…”

Section: Approximation Strategiesmentioning

confidence: 99%

Differentially Private Real-Time Data Publishing over Infinite Trajectory Streams

Cao

Yoshikawa

2016

IEICE Trans. Inf. & Syst.

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SUMMARYRecent emerging mobile and wearable technologies make it easy to collect personal spatiotemporal data such as activity trajectories in daily life. Publishing real-time statistics over trajectory streams produced by crowds of people is expected to be valuable for both academia and business, answering questions such as "How many people are in Kyoto Station now?" However, analyzing these raw data will entail risks of compromising individual privacy. -Differential Privacy has emerged as a well-known standard for private statistics publishing because of its guarantee of being rigorous and mathematically provable. However, since user trajectories will be generated infinitely, it is difficult to protect every trajectory under -differential privacy. On the other hand, in real life, not all users require the same level of privacy. To this end, we propose a flexible privacy model of -trajectory privacy to ensure every desired length of trajectory under protection of -differential privacy. We also design an algorithmic framework to publish -trajectory private data in real time. Experiments using four real-life datasets show that our proposed algorithms are effective and efficient.

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“…The usual approach to releasing differentially private data sets is based on histogram queries [31,32], that is, on approximating the data distribution by partitioning the data domain and counting the number of records in each partition set. To prevent the counts from leaking too much information they are computed in a differentially private manner.…”

Section: Related Work On Differentially Private Data Publishingmentioning

confidence: 99%

“…Moreover, since many works on differential privacy focus on preserving the utility of counting queries [35,31,32,27,28,29,30], we measured how the methods preserve the data distribution by building histograms of each attribute and comparing the distribution between the original and masked values according to the well-known Jensen-Shannon divergence (JSD) [56], which is symmetric and bounded in the 0..1 range. At a data set level, we averaged the divergence of all the attributes.…”

Section: Evaluation Measures and Experimentsmentioning

confidence: 99%

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Utility-preserving differentially private data releases via individual ranking microaggregation

2016

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Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as k-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them or because utility is only preserved for a restricted type and/or a limited number of queries. On the contrary, k-anonymity-like data releases make no assumptions on the uses of the protected data and, thus, do not restrict the number and type of doable analyses. Recently, some authors have proposed mechanisms to offer general-purpose differentially private data releases. This paper extends such works with a specific focus on the preservation of the utility of the protected data. Our proposal builds on microaggregation-based anonymization, which is more flexible and utility-preserving than alternative anonymization methods used in the literature, in order to reduce the amount of noise needed to satisfy differential privacy. In this way, we improve the utility of differentially private data releases. Moreover, the noise reduction we achieve does not depend on the size of the data set, but just on the number of attributes to be protected, which is a more desirable behavior for large data sets. The utility benefits brought by our proposal are empirically evaluated and compared with related works for several data sets and metrics.

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Differentially Private Histogram Publication

Cited by 103 publications

References 24 publications

Difference Privacy Histogram Release Based on Isotonic Regression

Difference Privacy Histogram Release Based on Isotonic Regression

Differentially Private Real-Time Data Publishing over Infinite Trajectory Streams

Utility-preserving differentially private data releases via individual ranking microaggregation

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