Journal of the ACM (JACM)
Communications of the ACM
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Web MIXes: a system for anonymous and unobservable Internet access
International workshop on Designing privacy enhancing technologies: design issues in anonymity and unobservability
Breaking the O(n1/(2k-1)) Barrier for Information-Theoretic Private Information Retrieval
FOCS '02 Proceedings of the 43rd Symposium on Foundations of Computer Science
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Replication is not needed: single database, computationally-private information retrieval
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
On Introducing Noise into the Bus-Contention Channel
SP '93 Proceedings of the 1993 IEEE Symposium on Security and Privacy
General constructions for information-theoretic private information retrieval
Journal of Computer and System Sciences
Access control to people location information
ACM Transactions on Information and System Security (TISSEC)
Distributed proxies for browsing privacy: a simulation of flocks
SAICSIT '05 Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries
Tor: the second-generation onion router
SSYM'04 Proceedings of the 13th conference on USENIX Security Symposium - Volume 13
Improving the Robustness of Private Information Retrieval
SP '07 Proceedings of the 2007 IEEE Symposium on Security and Privacy
The applicability of the perturbation based privacy preserving data mining for real-world data
Data & Knowledge Engineering
Data Collection with Self-Enforcing Privacy
ACM Transactions on Information and System Security (TISSEC)
Future Generation Computer Systems
Taking account of privacy when designing cloud computing services
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
De-anonymizing Social Networks
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Privacy Weaknesses in Biometric Sketches
SP '09 Proceedings of the 2009 30th IEEE Symposium on Security and Privacy
Obfuscation Mechanism in Conjunction with Tamper-Proof Module
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 02
Noise Injection for Search Privacy Protection
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Adapting Privacy-Preserving Computation to the Service Provider Model
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
Usable Privacy Controls for Blogs
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Examining the Shifting Nature of Privacy, Identities, and Impression Management with Web 2.0
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
A Privacy Manager for Cloud Computing
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Privacy of Value-Added Context-Aware Service Cloud
CloudCom '09 Proceedings of the 1st International Conference on Cloud Computing
Computationally private information retrieval with polylogarithmic communication
EUROCRYPT'99 Proceedings of the 17th international conference on Theory and application of cryptographic techniques
Cloud computing privacy concerns on our doorstep
Communications of the ACM
A firm foundation for private data analysis
Communications of the ACM
A trust-based noise injection strategy for privacy protection in cloud
Software—Practice & Experience
A Time-Series Pattern Based Noise Generation Strategy for Privacy Protection in Cloud Computing
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
An association probability based noise generation strategy for privacy protection in cloud computing
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
A personalised search approach for web service recommendation
International Journal of Ad Hoc and Ubiquitous Computing
International Journal of Ad Hoc and Ubiquitous Computing
Design and implementation of P2P reasoning system based on description logic
International Journal of Ad Hoc and Ubiquitous Computing
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Cloud computing promises an open environment where customers can deploy IT services in pay-as-you-go fashion while saving huge capital investment in their own IT infrastructure. Due to the openness, various malicious service providers can exist. Such service providers may record service requests from a customer and then collectively deduce the customer private information. Therefore, customers need to take certain actions to protect their privacy. Obfuscation with noise injection, that mixes noise service requests with real customer service requests so that service providers will be confused about which requests are real ones, is an effective approach in this regard if those request occurrence probabilities are about the same. However, current obfuscation with noise injection uses random noise requests. Due to the randomness it needs a large number of noise requests to hide the real ones so that all of their occurrence probabilities are about the same, i.e. service providers would be confused. In pay-as-you-go cloud environment, a noise request will cost the same as a real request. Hence, with the same level of confusion, i.e. customer privacy protection, the number of noise requests should be kept as few as possible. Therefore in this paper we develop a novel historical probability based noise generation strategy. Our strategy generates noise requests based on their historical occurrence probability so that all requests including noise and real ones can reach about the same occurrence probability, and then service providers would not be able to distinguish in between. Our strategy can significantly reduce the number of noise requests over the random strategy, by more than 90% as demonstrated by simulation evaluation.