- (Ashrafi et al., 2009) ⇒ Mafruz Zaman Ashrafi, and See Kiong Ng. (2009). “Collusion-resistant Anonymous Data Collection Method.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557034
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 a honest participant's Anonymity Measureanonymity 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.
|2009 CollusionResistantAnonymousData||Mafruz Zaman Ashrafi|
See Kiong Ng
|Collusion-resistant Anonymous Data Collection Method||KDD-2009 Proceedings||10.1145/1557019.1557034||2009|
|Author||Mafruz Zaman Ashrafi + and See Kiong Ng +|
|journal||Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining +|
|title||Collusion-resistant Anonymous Data Collection Method +|