Differentially private hierarchical count-of-counts histograms

Kuo, Yu-Hsuan; Chiu, Cho-Chun; Kifer, Daniel; Hay, Michael; Machanavajjhala, Ashwin

doi:10.14778/3236187.3236202

Cited by 21 publications

(13 citation statements)

References 29 publications

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“…There has been much work on how to efficiently express the joint distribution of multidimensional data [24, 26, 28, 32-34, 36, 38, 39, 41-43, 48, 50, 61, 65, 66, 72, 74, 81, 94, 95, 97]. There is also some work on histograms for special types of data, such as XML [12,18,98,100], spatial data [47, 55-57, 64, 68, 75, 90, 92, 93, 99, 102, 103], graph [29], string [44][45][46]71]; or for privacy [37,60,62].…”

Section: Database Scan-based Estimationmentioning

confidence: 99%

QuickSel: Quick Selectivity Learning with Mixture Models

Park

Zhong

Mozafari

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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Estimating the selectivity of a query is a key step in almost any cost-based query optimizer. Most of today's databases rely on histograms or samples that are periodically refreshed by re-scanning the data as the underlying data changes. Since frequent scans are costly, these statistics are often stale and lead to poor selectivity estimates. As an alternative to scans, query-driven histograms have been proposed, which refine the histograms based on the actual selectivities of the observed queries. Unfortunately, these approaches are either too costly to use in practice-i.e., require an exponential number of buckets-or quickly lose their advantage as they observe more queries. For example, the state-of-the-art technique requires 318,936 buckets (and over 8 seconds of refinement overhead per query) after observing only 300 queries.In this paper, we propose a selectivity learning framework, called QuickSel, which falls into the query-driven paradigm but does not use histograms. Instead, it builds an internal model of the underlying data, which can be refined significantly faster (e.g., only 1.9 milliseconds for 300 queries). This fast refinement allows QuickSel to continuously learn from each query and yield increasingly more accurate selectivity estimates over time. Unlike query-driven histograms, Quick-Sel relies on a mixture model and a new optimization algorithm for training its model. Our extensive experiments on two real-world datasets confirm that, given the same target accuracy, QuickSel is on average 254.6× faster than stateof-the-art query-driven histograms, including ISOMER and STHoles. Further, given the same space budget, QuickSel is on average 57.3% and 91.1% more accurate than periodicallyupdated histograms and samples, respectively.

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Section: Database Scan-based Estimationmentioning

confidence: 99%

QuickSel: Quick Selectivity Learning with Mixture Models

Park

Zhong

Mozafari

2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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“…There are also some studies investigating histogram publication under differential privacy, focusing on different types of data [17,18]. They also apply the underlying properties of these data to further reduce the degrees of noise [5,19,20]. However, they assume the existence of a trustable data curator to coordinate the data publication, which is usually infeasible for distributed data publication.…”

Section: Related Workmentioning

confidence: 99%

A Sampling‐Based Method for Highly Efficient Privacy‐Preserving Data Publication

Zheng

Duan

et al. 2021

Wireless Communications and Mobile Computing

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The data publication from multiple contributors has been long considered a fundamental task for data processing in various domains. It has been treated as one prominent prerequisite for enabling AI techniques in wireless networks. With the emergence of diversified smart devices and applications, data held by individuals becomes more pervasive and nontrivial for publication. First, the data are more private and sensitive, as they cover every aspect of daily life, from the incoming data to the fitness data. Second, the publication of such data is also bandwidth-consuming, as they are likely to be stored on mobile devices. The local differential privacy has been considered a novel paradigm for such distributed data publication. However, existing works mostly request the encoding of contents into vector space for publication, which is still costly in network resources. Therefore, this work proposes a novel framework for highly efficient privacy-preserving data publication. Specifically, two sampling-based algorithms are proposed for the histogram publication, which is an important statistic for data analysis. The first algorithm applies a bit-level sampling strategy to both reduce the overall bandwidth and balance the cost among contributors. The second algorithm allows consumers to adjust their focus on different intervals and can properly allocate the sampling ratios to optimize the overall performance. Both the analysis and the validation of real-world data traces have demonstrated the advancement of our work.

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“…The results will be used for apportioning representation in the US House of Representatives and dividing federal tax dollars between states, as well as for a multitude of other governmental activities at the national, state, and local levels. Data from the decennial census will also be used extensively by sociologists, economists, demographers, and other researchers, and it will also inform strategic decisions in the private and non-profit sectors, and facilitate the accurate weighting of subsequent population surveys for the next decade 3 .…”

Section: Introductionmentioning

confidence: 99%

Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff

Petti

Flaxman

2020

Gates Open Res

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Background: The 2020 US Census will use a novel approach to disclosure avoidance to protect respondents’ data, called TopDown. This TopDown algorithm was applied to the 2018 end-to-end (E2E) test of the decennial census. The computer code used for this test as well as accompanying exposition has recently been released publicly by the Census Bureau. Methods: We used the available code and data to better understand the error introduced by the E2E disclosure avoidance system when Census Bureau applied it to 1940 census data and we developed an empirical measure of privacy loss to compare the error and privacy of the new approach to that of a (non-differentially private) simple-random-sampling approach to protecting privacy. Results: We found that the empirical privacy loss of TopDown is substantially smaller than the theoretical guarantee for all privacy loss budgets we examined. When run on the 1940 census data, TopDown with a privacy budget of 1.0 was similar in error and privacy loss to that of a simple random sample of 50% of the US population. When run with a privacy budget of 4.0, it was similar in error and privacy loss of a 90% sample. Conclusions: This work fits into the beginning of a discussion on how to best balance privacy and accuracy in decennial census data collection, and there is a need for continued discussion.

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Differentially private hierarchical count-of-counts histograms

Abstract: We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms.

Cited by 21 publications

References 29 publications

QuickSel: Quick Selectivity Learning with Mixture Models

QuickSel: Quick Selectivity Learning with Mixture Models

A Sampling‐Based Method for Highly Efficient Privacy‐Preserving Data Publication

Differential privacy in the 2020 US census: what will it do? Quantifying the accuracy/privacy tradeoff

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