Privacy-aware data management in information networks

Hay, Michael; Liu, Kun; Miklau, Gerome; Pei, Jian; Terzi, Evimaria

doi:10.1145/1989323.1989453

Cited by 28 publications

(8 citation statements)

References 36 publications

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“…We have identified five tutorials [6,7,18,31,43] on differential privacy in the past five years, which are mainly from SIGMOD, KDD, and WIFS. Compared to tutorials before 2013 [7,18,31,43], the tutorial proposed for this venue will highlight recent techniques, as well as focus on the application of differential privacy to real problems and complex data types. While the building blocks of differentially pri-vate algorithms was the focus of [7], our tutorial has a larger scope of understanding the promise and limitations of differential privacy in real applications.…”

Section: History and Related Workmentioning

confidence: 99%

“…While the building blocks of differentially pri-vate algorithms was the focus of [7], our tutorial has a larger scope of understanding the promise and limitations of differential privacy in real applications. While [6,18,31] only focused on one specific application such as network data, or machine learning, we will also cover relational databases and trajectories. Moreover, we will also show how to customize differential privacy to meet the privacy requirements of these applications with complex data.…”

Section: History and Related Workmentioning

confidence: 99%

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Differential Privacy in the Wild

Machanavajjhala

Hay

2017

Proceedings of the 2017 ACM International Conference on Management of Data

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Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security, database, networks, machine learning, and statistics, over the last decade has resulted in a variety of privacy preserving algorithms for a number of analysis tasks. Despite maturing research efforts, the adoption of differential privacy by practitioners in industry, academia, or government agencies has so far been rare. Hence, in this tutorial, we will first describe the foundations of differentially private algorithm design that cover the state of the art in private computation on tabular data. In the second half of the tutorial we will highlight real world applications on complex data types, and identify research challenges in applying differential privacy to real world applications.

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Section: History and Related Workmentioning

confidence: 99%

Section: History and Related Workmentioning

confidence: 99%

Differential Privacy in the Wild

Machanavajjhala

Hay

2017

Proceedings of the 2017 ACM International Conference on Management of Data

Self Cite

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“…The goal is to provide statistical information about the data while preserving the privacy of users. Interesting works can be found, among others, in [22], [21] and [15].…”

Section: Privacy-preserving On Networkmentioning

confidence: 99%

A Summary of $$k$$ k -Degree Anonymous Methods for Privacy-Preserving on Networks

Casas-Roma

Herrera-Joancomartí

Torra

2014

Studies in Computational Intelligence

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In recent years there has been a significant raise in the use of graph-formatted data. For instance, social and healthcare networks present relationships among users, revealing interesting and useful information for researches and other third-parties. Notice that when someone wants to publicly release this information it is necessary to preserve the privacy of users who appear in these networks. Therefore, it is essential to implement an anonymization process in the data in order to preserve users' privacy. Anonymization of graph-based data is a problem which has been widely studied last years and several anonymization methods have been developed. In this chapter we summarize some methods for privacy-preserving on networks, focusing on methods based on the kanonymity model. We also compare the results of some k-degree anonymous methods on our experimental set up, by evaluating the data utility and the information loss on real networks.

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“…Many graph analyses satisfy privacy definitions other than differential privacy [4]. These definitions generally do not exhibit the robustness of differential privacy, and a comparison is beyond the scope of this note.…”

Section: Related Workmentioning

confidence: 99%

A workflow for differentially-private graph synthesis

Proserpio

Goldberg

McSherry

2012

Proceedings of the 2012 ACM Workshop on Workshop on Online Social Networks

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We present a new workflow for differentially-private publication of graph topologies. First, we produce differentiallyprivate measurements of interesting graph statistics using our new version of the PINQ programming language, Weighted PINQ, which is based on a generalization of differential privacy to weighted sets. Next, we show how to generate graphs that fit any set of measured graph statistics, even if they are inconsistent (due to noise), or if they are only indirectly related to actual statistics that we want our synthetic graph to preserve. We combine the answers to Weighted PINQ queries with an incremental evaluator (Markov Chain Monte Carlo (MCMC)) to synthesize graphs where the statistic of interest aligns with that of the protected graph. This paper presents our preliminary results; we show how to cast a few graph statistics (degree distribution, edge multiplicity, joint degree distribution) as queries in Weighted PINQ, and then present experimental results synthesizing graphs generated from answers to these queries.

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Privacy-aware data management in information networks

Cited by 28 publications

References 36 publications

Differential Privacy in the Wild

Differential Privacy in the Wild

A Summary of $$k$$ k -Degree Anonymous Methods for Privacy-Preserving on Networks

A workflow for differentially-private graph synthesis

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