User Data Sharing Frameworks: A Blockchain-Based Incentive Solution

User Data Sharing Frameworks: A Blockchain- Based Incentive Solution Ajay Kumar Shrestha Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan, Canada ajay.shrestha@usask.ca Julita Vassileva Department of Computer Science University of Saskatchewan Saskatoon, Saskatchewan, Canada julita.vassileva @usask.ca Abstract— Currently, there is no universal method to track who shared what, with whom, when and for what purposes in a verifiable way to create an individual incentive for data owners. A platform that allows data owners to control, delete, and get rewards from sharing their data would be an important enabler of user data-sharing. We propose a usable blockchain- and smart contracts-based framework that allows users to store research data locally and share without losing control and ownership of it. We have created smart contracts for building automatic verification of the conditions for data access that also naturally supports building up a verifiable record of the provenance, incentives for users to share their data and accountability of access. The paper presents a review of the existing work of research data sharing, the proposed blockchain-based framework and an evaluation of the framework by measuring the transaction cost for smart contracts deployment. The results show that nodes responded quickly in all tested cases with a befitting transaction cost. Keywords— Data Sharing, User-controlled, Privacy, Trust, Security, Blockchain, Smart Contract, Ethereum, MultiChain, Transaction, Incentives I. INTRODUCTION The internet from its inception was aimed to facilitate users in sharing data, and it enabled it through centralized (e.g. FTP) or decentralized (e.g. email) services. With Web 2.0 [1] or the social web, it became very easy to share creative products on social sites (user-generated content on YouTube, Wikipedia, blogs, as well as microblogging tools like Twitter and Facebook). To achieve profit in the business model, secondary data associated with the users’ profile and behaviour are being collected and shared among the enterprises, for a personalized advertisement that targets the users based on that information. Much of the data are contributed voluntarily by the user; others are obtained by the system from the observation of user activities or inferred through advanced analysis of volunteered or observed data [2]. In different domains such as tourism, e-commerce, news aggregators, dating services etc., the data analytics and the personalization enhances the users’ interaction with the system, and the overall quality of services being offered to the users. The applications that aim for personalization need to gather information about their users and create predictive user models, used to adapt their functionality, presentation, or offers to the specific users’ requirements [3]. The process of user modeling requires collecting user data and making inferences from this data by both finding patterns and similarities across the many users of a service, or by abstracting user features and building user profiles from the history of the interaction of a user [4]. This is a slow process prone to the “cold start” problem, and its variants (new user, or new product/feature) [5]. To speed up learning about their users, applications can share relevant data about the same user with other applications, leading to the need for sharing user interaction data and user profiles [6]. This sharing is very problematic ethically, and the recent EU’s General Data Protection Regulation (GDPR) makes explicit the problems and trade-offs related to user privacy and control over data, as well as fairness while preserving the richness of data. Our research shows that these problems have not been addressed by different proposed architectures and methods for user profile data sharing. Most importantly, in the scientific research domain, research data sharing practices are much needed to maximize the knowledge gains from the research efforts of millions of researchers. Sharing research data can reduce duplicative trials and accelerate discovery. In medicine and healthcare, both personalized patient care and medical research can benefit from ethical and privacy-preserving sharing of patient data and data from clinical trials [7]. A flexible mechanism for obtaining and renewing consent for data use and sharing is required that provides appropriate and meaningful incentives to capitalize from data sharing and ensures transparency for users to be aware of which of their data has been accessed, by whom, for what purpose and under what conditions. Currently, there is no single trusted authority to ensure ethical user data sharing. It has been demonstrated that the creativity and the advancement of the technologies have given birth to many computational backbones to ensure privacy and data sharing model that include cl

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