Using Public and Private Blockchains for Secure Data Sharing and Analytics

Data Sharing has become a prominent issue in today’s world as more and more data is col- lected for various reasons. Due to privacy, and security concerns and regulatory compliance issues, the conditions under which the sharing occurs needs to be carefully specified and managed. Voter registration, financial compliance, healthcare management systems, insur- ance companies, monetary banks, etc. are just few examples among many that embrace data sharing at the heart of their ecosystem. These systems require to share data as per legal agreements and with the right entities. In some use cases, instead of sharing the actual data, we may need to share data analytics results based on the shared data, (e.g., computing auction results based on private bids) and make sure those results are secure. In another use case, we may need to run Machine Learning(ML) algorithms to generate ML models on the shared data. In each of these use cases, along with the compliance of regulatory standards, we need to ensure that data privacy is preserved and finally, offenders that do not comply with the requirements are automatically penalized. In this dissertation, we consider each of these scenarios and address the privacy, regulatory compliance and security challenges by incen- tivizing honest behavior by using Blockchains and Trusted Hardware. First, we present an alternative for tracking, managing and especially adjudicating data sharing agreements using smart contracts and blockchain technology. Next, we show how to leverage both public and private blockchain infrastructures to enable efficient, privacy en- hancing and accountable digital auctions. Furthermore, we delve into the field of Federated Learning (FL) and show how to secure machine learning models using a hybrid blockchain architecture that discourages backdoor attacks by detecting and punishing the attackers. To further secure shared data, we show how we can use the Trusted Execution Environ- ments(TEEs) to enable efficient, privacy enhancing and secure applications by implementing oblivious execution while running the smart contracts inside trusted enclaves again in the context of auctions. Finally, to secure Federated Learning models against privacy leakage attacks over Blockchain based smart contracts, we show how to take advantage of TEEs to run those smart contracts inside trusted enclaves without any significant impact on efficiency.