Security-control methods for statistical databases: a comparative study
ACM Computing Surveys (CSUR)
Journal of the ACM (JACM)
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The statistical security of a statistical database
ACM Transactions on Database Systems (TODS)
Cryptography and data security
Cryptography and data security
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Modern Information Retrieval
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEEE Computational Science & Engineering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Tools for privacy preserving distributed data mining
ACM SIGKDD Explorations Newsletter
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Privacy-preserving Distributed Clustering using Generative Models
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Privacy-preserving k-means clustering over vertically partitioned data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient elastic burst detection in data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
IEEE Transactions on Knowledge and Data Engineering
State-of-the-art in privacy preserving data mining
ACM SIGMOD Record
Privacy-Preserving Outlier Detection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Privacy-preserving clustering with distributed EM mixture modeling
Knowledge and Information Systems
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A Tree-Based Data Perturbation Approach for Privacy-Preserving Data Mining
IEEE Transactions on Knowledge and Data Engineering
Anonymizing Classification Data for Privacy Preservation
IEEE Transactions on Knowledge and Data Engineering
Data & Knowledge Engineering
Privacy preserving clustering on horizontally partitioned data
Data & Knowledge Engineering
The applicability of the perturbation based privacy preserving data mining for real-world data
Data & Knowledge Engineering
How to generate and exchange secrets
SFCS '86 Proceedings of the 27th Annual Symposium on Foundations of Computer Science
Detecting Aggregate Bursts from Scaled Bins within the Context of Privacy
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Fast burst correlation of financial data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Trust-based privacy preservation for peer-to-peer data sharing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Scaling-invariant boundary image matching using time-series matching techniques
Data & Knowledge Engineering
Extending l-diversity to generalize sensitive data
Data & Knowledge Engineering
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Surprisingly, privacy preservation in the context of streaming data has received limited attention from computer scientists. In this paper, we consider privacy preservation in the context of independently owned, distributed data streams. Specifically, we want to protect the privacy of each individual participant's data stream while identifying bursts that exist across participant streams. We define two types of privacy breaches, data breaches and envelope breaches. In order to protect individual data, each participant transforms large subsets of the stream into small vectors that approximate the stream. These small vectors are calculated by summing coefficients of wavelet transforms at different resolutions. The participants share their vectors using bursty, self-eliminating noise. The combined participant vectors can then be used to detect bursts. We find that our approach leads to accurate burst detection results with reduced communication costs. We demonstrate these findings using both real and synthetic data.