Privacy-preserving negotiations
Carlos Cotrini
Prof. Dr. Joachim Buhmann
The rise of online marketplaces in the sharing economy has brought about a new way for individuals to exchange goods and services by finding matching users. Platforms such as car sharing services and online dating services have gained popularity due to their ability to connect users with similar interests.
In this research proposal, we aim to formalize this search process using contextual bandits, where the learner searches for compatible users and the arms are other users. However, an important aspect that differentiates this setting from previous work on bandits is that the arm features are not readily visible to the learner. Furthermore, arms may be sensitive about their privacy, making it challenging to strike a balance between maximizing the learner's reward and respecting the arms' privacy requirements.
To address these challenges, we propose combining the UCB algorithm with local differential privacy to achieve reasonable regret bounds for the learner while respecting the arms' privacy requirements. The ultimate goal of this research is to provide a solution that empowers individuals to find compatible users while maintaining their privacy.
Proposal
The rise of online marketplaces in the sharing economy has brought about a new way for individuals to exchange goods and services by finding matching users. Platforms such as car sharing services and online dating services have gained popularity due to their ability to connect users with similar interests.
In this research proposal, we aim to formalize this search process using contextual bandits, where the learner searches for compatible users and the arms are other users. However, an important aspect that differentiates this setting from previous work on bandits is that the arm features are not readily visible to the learner. Furthermore, arms may be sensitive about their privacy, making it challenging to strike a balance between maximizing the learner's reward and respecting the arms' privacy requirements.
To address these challenges, we propose combining the UCB algorithm with local differential privacy to achieve reasonable regret bounds for the learner while respecting the arms' privacy requirements. The ultimate goal of this research is to provide a solution that empowers individuals to find compatible users while maintaining their privacy.
Proposal