A mobile agent-based privacy protection mechanism in solving multi-party computation problems

A multi-party computation (MPC) allows n parties to compute an agreed-upon function of their inputs and every party learns the correct function output. To solve a multi-party computation problem (MPCP), the participants may need to share their private data (inputs) between one another, resulting in data privacy loss. The key research issue that has been addressed in this thesis is how to solve multi-party computation problems without disclosing anyone’s private data to others. Firstly, by studying and analyzing the traditional computational models, we have devised a privacy loss model for multi-party computation problems and proposed a novel metric, called the Min privacy metric, for quantitatively measuring the amount of data privacy loss in solving the MPCPs. Then, we have presented a mobile agentbased scheduling algorithm that applies pseudonymization technique to reduce data privacy loss. Finally, we have proposed the security system design, including security policies and security architecture, of an agent server platform for enhancing data privacy protection while solving the MPCPs. The privacy loss model has identified three factors affecting the amount of privacy loss in solving the MPCPs: (1) the fraction of private data which is shared with others, (2) the probability of associating the shared private data with the data subject, and (3) the probability of disclosing the shared private data to unauthorized parties. Privacy loss can be reduced by any mechanisms which reduces the values of any of the three factors. The proposed Min privacy metric accounts for the number of participants that lose their private data and the amount of private data disclosed to unauthorized parties, regardless of how many parties they are revealed to. Existing scheduling algorithms aim for a global objective function. As a result, they incur performance penalties in computational complexity and data privacy. This thesis describes a mobile agent-based scheduling scheme called Efficient and Privacyaware Meeting Scheduling (EPMS), which results in a tradeoff among complexity,

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