Hiding in the crowd: Privacy preservation on evolving streams through correlation tracking

Feifei Li, Jimeng Sun, Spiros Papadimitriou, George A. Mihaila, Ioana Stanoi

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

40 Citations (Scopus)

Abstract

We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on random perturbation of the data values. However, streams pose additional challenges. First, analysis of the data has to be performed incrementally, using limited processing time and buffer space, making batch approaches unsuitable. Second, the characteristics of streams evolve over time. Consequently, approaches based on global analysis of the data are not adequate. We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in the sense that it is very hard to remove. Our techniques achieve much better results than previous static, global approaches, while requiring limited processing time and memory. We provide both a mathematical analysis and experimental evaluation on real data to validate the correctness, efficiency, and effectiveness of our algorithms.

Original languageEnglish (US)
Title of host publication23rd International Conference on Data Engineering, ICDE 2007
Pages686-695
Number of pages10
DOIs
StatePublished - Sep 24 2007
Externally publishedYes
Event23rd International Conference on Data Engineering, ICDE 2007 - Istanbul, Turkey
Duration: Apr 15 2007Apr 20 2007

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other23rd International Conference on Data Engineering, ICDE 2007
CountryTurkey
CityIstanbul
Period4/15/074/20/07

Fingerprint

Processing
Autocorrelation
Data storage equipment

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

Cite this

Li, F., Sun, J., Papadimitriou, S., Mihaila, G. A., & Stanoi, I. (2007). Hiding in the crowd: Privacy preservation on evolving streams through correlation tracking. In 23rd International Conference on Data Engineering, ICDE 2007 (pp. 686-695). [4221717] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2007.367914
Li, Feifei ; Sun, Jimeng ; Papadimitriou, Spiros ; Mihaila, George A. ; Stanoi, Ioana. / Hiding in the crowd : Privacy preservation on evolving streams through correlation tracking. 23rd International Conference on Data Engineering, ICDE 2007. 2007. pp. 686-695 (Proceedings - International Conference on Data Engineering).
@inproceedings{e4a2fd6e3c1b4f7bbf554dc6908bea74,
title = "Hiding in the crowd: Privacy preservation on evolving streams through correlation tracking",
abstract = "We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on random perturbation of the data values. However, streams pose additional challenges. First, analysis of the data has to be performed incrementally, using limited processing time and buffer space, making batch approaches unsuitable. Second, the characteristics of streams evolve over time. Consequently, approaches based on global analysis of the data are not adequate. We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in the sense that it is very hard to remove. Our techniques achieve much better results than previous static, global approaches, while requiring limited processing time and memory. We provide both a mathematical analysis and experimental evaluation on real data to validate the correctness, efficiency, and effectiveness of our algorithms.",
author = "Feifei Li and Jimeng Sun and Spiros Papadimitriou and Mihaila, {George A.} and Ioana Stanoi",
year = "2007",
month = "9",
day = "24",
doi = "10.1109/ICDE.2007.367914",
language = "English (US)",
isbn = "1424408032",
series = "Proceedings - International Conference on Data Engineering",
pages = "686--695",
booktitle = "23rd International Conference on Data Engineering, ICDE 2007",

}

Li, F, Sun, J, Papadimitriou, S, Mihaila, GA & Stanoi, I 2007, Hiding in the crowd: Privacy preservation on evolving streams through correlation tracking. in 23rd International Conference on Data Engineering, ICDE 2007., 4221717, Proceedings - International Conference on Data Engineering, pp. 686-695, 23rd International Conference on Data Engineering, ICDE 2007, Istanbul, Turkey, 4/15/07. https://doi.org/10.1109/ICDE.2007.367914

Hiding in the crowd : Privacy preservation on evolving streams through correlation tracking. / Li, Feifei; Sun, Jimeng; Papadimitriou, Spiros; Mihaila, George A.; Stanoi, Ioana.

23rd International Conference on Data Engineering, ICDE 2007. 2007. p. 686-695 4221717 (Proceedings - International Conference on Data Engineering).

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

TY - GEN

T1 - Hiding in the crowd

T2 - Privacy preservation on evolving streams through correlation tracking

AU - Li, Feifei

AU - Sun, Jimeng

AU - Papadimitriou, Spiros

AU - Mihaila, George A.

AU - Stanoi, Ioana

PY - 2007/9/24

Y1 - 2007/9/24

N2 - We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on random perturbation of the data values. However, streams pose additional challenges. First, analysis of the data has to be performed incrementally, using limited processing time and buffer space, making batch approaches unsuitable. Second, the characteristics of streams evolve over time. Consequently, approaches based on global analysis of the data are not adequate. We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in the sense that it is very hard to remove. Our techniques achieve much better results than previous static, global approaches, while requiring limited processing time and memory. We provide both a mathematical analysis and experimental evaluation on real data to validate the correctness, efficiency, and effectiveness of our algorithms.

AB - We address the problem of preserving privacy in streams, which has received surprisingly limited attention. For static data, a well-studied and widely used approach is based on random perturbation of the data values. However, streams pose additional challenges. First, analysis of the data has to be performed incrementally, using limited processing time and buffer space, making batch approaches unsuitable. Second, the characteristics of streams evolve over time. Consequently, approaches based on global analysis of the data are not adequate. We show that it is possible to efficiently and effectively track the correlation and autocorrelation structure of multivariate streams and leverage it to add noise which maximally preserves privacy, in the sense that it is very hard to remove. Our techniques achieve much better results than previous static, global approaches, while requiring limited processing time and memory. We provide both a mathematical analysis and experimental evaluation on real data to validate the correctness, efficiency, and effectiveness of our algorithms.

UR - http://www.scopus.com/inward/record.url?scp=34548723856&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=34548723856&partnerID=8YFLogxK

U2 - 10.1109/ICDE.2007.367914

DO - 10.1109/ICDE.2007.367914

M3 - Conference contribution

AN - SCOPUS:34548723856

SN - 1424408032

SN - 9781424408030

T3 - Proceedings - International Conference on Data Engineering

SP - 686

EP - 695

BT - 23rd International Conference on Data Engineering, ICDE 2007

ER -

Li F, Sun J, Papadimitriou S, Mihaila GA, Stanoi I. Hiding in the crowd: Privacy preservation on evolving streams through correlation tracking. In 23rd International Conference on Data Engineering, ICDE 2007. 2007. p. 686-695. 4221717. (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2007.367914