The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. Increasing security concerns have led to a surge in work on practical secure multi-party computation protocols. However, most are only proven secure under the semihonest model, and security under this adversary model is insufficient for most applications in the field of privacypreserving data mining. In this paper, we present the full spectrum of the accountable computing (AC) framework, which is sufficient or practical for many applications without the complexity and cost of an SMC-protocol under the malicious model. Furthermore, to show the applicability of the AC-framework, we present an application under this framework regarding privacy-preserving mining frequent itemsets.
Jiang and Clifton REF presented the Accountable Computing (AC) framework to detect malicious behaviors with the help of a third independent entity.
Ac-framework for privacypreserving collaboration
In Proceedings of the 2007 SIAM International Conference on Data Mining
Computer Science