Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Chain: operator scheduling for memory minimization in data stream systems
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Queueing analysis of relational operators for continuous data streams
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
High-performance complex event processing over streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
STAGGER: Periodicity Mining of Data Streams Using Expanding Sliding Windows
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Efficient pattern matching over event streams
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Butterfly: Protecting Output Privacy in Stream Mining
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ZStream: a cost-based query processor for adaptively detecting composite events
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
CHAOS: A Data Stream Analysis Architecture for Enterprise Applications
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Inapproximability of maximum weighted edge biclique and its applications
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
Optimal sampling from distributed streams
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Differentially private aggregation of distributed time-series with transformation and encryption
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Hiding Sequential and Spatiotemporal Patterns
IEEE Transactions on Knowledge and Data Engineering
A Privacy-Preserving Location Monitoring System for Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Active complex event processing: applications in real-time health care
Proceedings of the VLDB Endowment
On the complexity of privacy-preserving complex event processing
Proceedings of the thirtieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
MaskIt: privately releasing user context streams for personalized mobile applications
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Monitoring web browsing behavior with differential privacy
Proceedings of the 23rd international conference on World wide web
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Complex Event Processing (CEP) has emerged as a technology for monitoring event streams in search of user specified event patterns. When a CEP system is deployed in sensitive environments the user may wish to mitigate leaks of private information while ensuring that useful nonsensitive patterns are still reported. In this paper we consider how to suppress events in a stream to reduce the disclosure of sensitive patterns while maximizing the detection of nonsensitive patterns. We first formally define the problem of utility-maximizing event suppression with privacy preferences, and analyze its computational hardness. We then design a suite of real-time solutions to solve this problem. Our first solution optimally solves the problem at the event-type level. The second solution, at the event-instance level, further optimizes the event-type level solution by exploiting runtime event distributions using advanced pattern match cardinality estimation techniques. Our user study and experimental evaluation over both real-world and synthetic event streams show that our algorithms are effective in maximizing utility yet still efficient enough to offer near real-time system responsiveness.