research-article

Privacy-preserving OLAP based on output perturbation across multiple sites

Authors:

Yunhai Tong,

Guohui Sun,

Peng Zhang,

Shiwei TangAuthors Info & Claims

PST '06: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services

Article No.: 46, Page 1

https://doi.org/10.1145/1501434.1501489

Published: 30 October 2006 Publication History

Get Access

Abstract

No abstract available.

Cited By

View all

Cuzzocrea ABertino E(2014)A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAPProceedings of the Confederated International Workshops on On the Move to Meaningful Internet Systems: OTM 2014 Workshops - Volume 884210.1007/978-3-662-45550-0_16(117-136)Online publication date: 27-Oct-2014
https://dl.acm.org/doi/10.1007/978-3-662-45550-0_16
Cuzzocrea ABertino E(2014)Theory-Inspired Optimizations for Privacy Preserving Distributed OLAP AlgorithmsProceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems - Volume 848010.1007/978-3-319-07617-1_39(439-453)Online publication date: 11-Jun-2014
https://dl.acm.org/doi/10.1007/978-3-319-07617-1_39
Cuzzocrea ABertino E(2013)Further Theoretical Contributions to a Privacy Preserving Distributed OLAP FrameworkProceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference10.1109/COMPSAC.2013.39(234-239)Online publication date: 22-Jul-2013
https://dl.acm.org/doi/10.1109/COMPSAC.2013.39
Show More Cited By

Recommendations

Privacy-Preserving OLAP: An Information-Theoretic Approach

We address issues related to the protection of private information in Online Analytical Processing (OLAP) systems, where a major privacy concern is the adversarial inference of private information from OLAP query answers. Most previous work on privacy-...
Privacy preserving OLAP
SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data

We present techniques for privacy-preserving computation of multidimensional aggregates on data partitioned across multiple clients. Data from different clients is perturbed (randomized) in order to preserve privacy before it is integrated at the ...
A Robust Sampling-Based Framework for Privacy Preserving OLAP
DaWaK '08: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery

A robust sampling-based framework for privacy preserving OLAP is introduced and experimentally assessed in this paper. The most distinctive characteristic of the proposed framework consists in adopting an innovative <em>privacy OLAP notion</em>, which ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

PST '06: Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services

October 2006

389 pages

ISBN:1595936041

DOI:10.1145/1501434

General Chair:
Greg Sprague
National Research Council, Fredericton, New Brunswick, Canada
,
Program Chairs:
Bernadette Schell
UOIT, Oshawa, Ontario, Canada
,
Wilfred Fong
UOIT, Oshawa, Ontario, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Research-article

Conference

PST06

PST06: International Conference on Privacy, Security and Trust

October 30 - November 1, 2006

Ontario, Markham, Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Cuzzocrea ABertino E(2014)A Comprehensive Theoretical Framework for Privacy Preserving Distributed OLAPProceedings of the Confederated International Workshops on On the Move to Meaningful Internet Systems: OTM 2014 Workshops - Volume 884210.1007/978-3-662-45550-0_16(117-136)Online publication date: 27-Oct-2014
https://dl.acm.org/doi/10.1007/978-3-662-45550-0_16
Cuzzocrea ABertino E(2014)Theory-Inspired Optimizations for Privacy Preserving Distributed OLAP AlgorithmsProceedings of the 9th International Conference on Hybrid Artificial Intelligence Systems - Volume 848010.1007/978-3-319-07617-1_39(439-453)Online publication date: 11-Jun-2014
https://dl.acm.org/doi/10.1007/978-3-319-07617-1_39
Cuzzocrea ABertino E(2013)Further Theoretical Contributions to a Privacy Preserving Distributed OLAP FrameworkProceedings of the 2013 IEEE 37th Annual Computer Software and Applications Conference10.1109/COMPSAC.2013.39(234-239)Online publication date: 22-Jul-2013
https://dl.acm.org/doi/10.1109/COMPSAC.2013.39
Cuzzocrea ABertino ESaccà D(2012)Towards a theory for privacy preserving distributed OLAPProceedings of the 2012 Joint EDBT/ICDT Workshops10.1145/2320765.2320826(221-226)Online publication date: 30-Mar-2012
https://dl.acm.org/doi/10.1145/2320765.2320826

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

Cited By

Recommendations

Privacy-Preserving OLAP: An Information-Theoretic Approach