Privacy-preserving data sharing and utilization between entities
In this thesis, we aim to enable privacy-preserving data sharing between entities and propose two systems for this purpose: (i) a veri able computation scheme that enables privacy-preserving similarity computation in the malicious setting and (ii) a privacy-preserving link prediction scheme in the semi-honest setting. Both of these schemes preserve the privacy of the involving parties, while performing some tasks to improve the service quality. In veri able computation, we propose a centralized system, which involves a client and multiple servers. We speci cally focus on the case, in which we want to compute the similarity of a patient's data across several hospitals. Client, who is the hospital that owns the patient data, sends the query to multiple servers, which are di erent hospitals. Client wants to nd similar patients in these hospitals in order to learn about the treatment techniques applied to those patients. In our link prediction scheme, we have two social networks with common users in both of them. We choose two nodes to perform link prediction between them. We perform link prediction in a privacy-preserving way so that neither of the networks learn the structure of the other network. We apply di erent metrics to de ne the similarity of the nodes. While doing this, we utilize privacy-preserving integer comparison.