The dining cryptographers problem: unconditional sender and recipient untraceability
Journal of Cryptology
Unconditional sender and recipient untraceability in spite of active attacks
EUROCRYPT '89 Proceedings of the workshop on the theory and application of cryptographic techniques on Advances in cryptology
Crowds: anonymity for Web transactions
ACM Transactions on Information and System Security (TISSEC)
Untraceable electronic mail, return addresses, and digital pseudonyms
Communications of the ACM
Hordes: a multicast based protocol for anonymity
Journal of Computer Security
A Practical Public Key Cryptosystem Provably Secure Against Adaptive Chosen Ciphertext Attack
CRYPTO '98 Proceedings of the 18th Annual International Cryptology Conference on Advances in Cryptology
Limiting privacy breaches in privacy preserving data mining
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Privacy preserving mining of association rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Information sharing across private databases
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Anonymous Connections and Onion Routing
SP '97 Proceedings of the 1997 IEEE Symposium on Security and Privacy
k-anonymous message transmission
Proceedings of the 10th ACM conference on Computer and communications security
Anonymity-preserving data collection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Efficient anonymity-preserving data collection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data collection with self-enforcing privacy
Proceedings of the 13th ACM conference on Computer and communications security
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The availability and the accuracy of the data dictate the success of a data mining application. Increasingly, there is a need to resort to on-line data collection to address the problem of data availability. However, participants in on-line data collection applications are naturally distrustful of the data collector as well as their peer respondents, resulting in inaccurate data collected as the respondents refuse to provide truthful data in fear of collusion attacks. The current anonymity-preserving solutions for on-line data collection are unable to adequately resist such attacks in a scalable fashion. In this paper, we present an efficient anonymous data collection protocol for a malicious environment such as the Internet. The protocol employs cryptographic and random shuffling techniques to preserve participants' anonymity. The proposed method is collusion-resistant and guarantees that an attacker will be unable to breach an honest participant's anonymity unless she controls all N-1 participants. In addition, our method is efficient and achieved 15-42% communication overhead reduction in comparison to the prior state-of-the-art methods.