Incentive-driven and privacy-preserving collaborative computing

The Secure Multi-Party Computation (SMC) model provides means for balancing the use and confidentiality of distributed data. This is especially important in the field of privacy-preserving data analysis (PPDA). 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 semi-honest model, and security under this adversary model is insufficient for many PPDA applications. SMC protocols under the malicious adversary model generally have impractically high complexities for PPDA. We propose an accountable computing (AC) framework that enables liability for privacy compromise to be assigned to the responsible party without the complexity and cost of an SMC-protocol under the malicious model. We show how to transform two generic semi-honest two-party protocols into protocols satisfying the AC-framework and present applications related to the framework. Even though SMC-based techniques guarantee that nothing other than the final result is revealed, it is impossible to verify whether participating parties are truthful about their private input data. In other words, unless proper incentives are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs. This raises the question of how to design incentive-driven privacy-preserving data analysis techniques that motivate participating parties to provide truthful input data. Therefore, this thesis also develops key theorems, and based on these theorems, we analyze certain important privacy-preserving data analysis tasks that could be conducted in a way that telling the truth is the best choice for any participating party.