Differential privacy in data publication and analysis

Yin Yang, Zhenjie Zhang, Gerome Miklau, Marianne Winslett, Xiaokui Xiao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

32 Citations (Scopus)

Abstract

Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.

Original languageEnglish
Title of host publicationSIGMOD '12 - Proceedings of the International Conference on Management of Data
Pages601-605
Number of pages5
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12 - Scottsdale, AZ, United States
Duration: 21 May 201224 May 2012

Other

Other2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12
CountryUnited States
CityScottsdale, AZ
Period21/5/1224/5/12

Fingerprint

Data privacy
Computer science

Keywords

  • data analysis
  • differential privacy
  • privacy-preserving data publication
  • query processing

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Yang, Y., Zhang, Z., Miklau, G., Winslett, M., & Xiao, X. (2012). Differential privacy in data publication and analysis. In SIGMOD '12 - Proceedings of the International Conference on Management of Data (pp. 601-605) https://doi.org/10.1145/2213836.2213910

Differential privacy in data publication and analysis. / Yang, Yin; Zhang, Zhenjie; Miklau, Gerome; Winslett, Marianne; Xiao, Xiaokui.

SIGMOD '12 - Proceedings of the International Conference on Management of Data. 2012. p. 601-605.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, Y, Zhang, Z, Miklau, G, Winslett, M & Xiao, X 2012, Differential privacy in data publication and analysis. in SIGMOD '12 - Proceedings of the International Conference on Management of Data. pp. 601-605, 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12, Scottsdale, AZ, United States, 21/5/12. https://doi.org/10.1145/2213836.2213910
Yang Y, Zhang Z, Miklau G, Winslett M, Xiao X. Differential privacy in data publication and analysis. In SIGMOD '12 - Proceedings of the International Conference on Management of Data. 2012. p. 601-605 https://doi.org/10.1145/2213836.2213910
Yang, Yin ; Zhang, Zhenjie ; Miklau, Gerome ; Winslett, Marianne ; Xiao, Xiaokui. / Differential privacy in data publication and analysis. SIGMOD '12 - Proceedings of the International Conference on Management of Data. 2012. pp. 601-605
@inproceedings{1c362e4a41d74d78a0e19fa118e936b2,
title = "Differential privacy in data publication and analysis",
abstract = "Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.",
keywords = "data analysis, differential privacy, privacy-preserving data publication, query processing",
author = "Yin Yang and Zhenjie Zhang and Gerome Miklau and Marianne Winslett and Xiaokui Xiao",
year = "2012",
doi = "10.1145/2213836.2213910",
language = "English",
isbn = "9781450312479",
pages = "601--605",
booktitle = "SIGMOD '12 - Proceedings of the International Conference on Management of Data",

}

TY - GEN

T1 - Differential privacy in data publication and analysis

AU - Yang, Yin

AU - Zhang, Zhenjie

AU - Miklau, Gerome

AU - Winslett, Marianne

AU - Xiao, Xiaokui

PY - 2012

Y1 - 2012

N2 - Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.

AB - Data privacy has been an important research topic in the security, theory and database communities in the last few decades. However, many existing studies have restrictive assumptions regarding the adversary's prior knowledge, meaning that they preserve individuals' privacy only when the adversary has rather limited background information about the sensitive data, or only uses certain kinds of attacks. Recently, differential privacy has emerged as a new paradigm for privacy protection with very conservative assumptions about the adversary's prior knowledge. Since its proposal, differential privacy had been gaining attention in many fields of computer science, and is considered among the most promising paradigms for privacy-preserving data publication and analysis. In this tutorial, we will motivate its introduction as a replacement for other paradigms, present the basics of the differential privacy model from a database perspective, describe the state of the art in differential privacy research, explain the limitations and shortcomings of differential privacy, and discuss open problems for future research.

KW - data analysis

KW - differential privacy

KW - privacy-preserving data publication

KW - query processing

UR - http://www.scopus.com/inward/record.url?scp=84862652273&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862652273&partnerID=8YFLogxK

U2 - 10.1145/2213836.2213910

DO - 10.1145/2213836.2213910

M3 - Conference contribution

AN - SCOPUS:84862652273

SN - 9781450312479

SP - 601

EP - 605

BT - SIGMOD '12 - Proceedings of the International Conference on Management of Data

ER -