Data sharing practices are much needed to strike a balance between user privacy, user experience, and profit. Different parties collect user data, for example, companies offering apps, social networking sites, and others, whose primary motive is an enhanced business model while giving optimal services to the end-users. However, the collection of user data is associated with serious privacy and security issues. The sharing platform also needs an effective incentive mechanism to realize transparent access to the user data while distributing fair incentives. The emerging literature on the topic includes decentralized data sharing approaches. However, there has been no universal method to track who shared what, to whom, when, for what purpose and under what condition in a verifiable manner until recently, when the distributed ledger technologies emerged to become the most effective means for designing a decentralized peer-to-peer network. This Ph.D. research includes an engineering approach for specifying the operations for designing incentives and user-controlled data-sharing platforms. The thesis presents a series of empirical studies and proposes novel blockchains- and smart contracts-based DUDS (Decentralized User Data Sharing) framework conceptualizing user-controlled data sharing practices. The DUDS framework supports immutability, authenticity, enhanced security, trusted records and is a promising means to share user data in various domains, including among researchers, customer data in e-commerce, tourism applications, etc. The DUDS framework is evaluated via performance analyses and user studies. The extended Technology Acceptance Model and a Trust-Privacy-Security Model are used to evaluate the usability of the DUDS framework. The evaluation allows uncovering the role of different factors affecting user intention to adopt data-sharing platforms. The results of the evaluation point to guidelines and methods for embedding privacy, user transparency, control, and incentives from the start in the design of a data-sharing framework to provide a platform that users can trust to protect their data while allowing them to control it and share it in the ways they want
