Abstract
This paper proposes a novel data-owner-driven privacy-aware cloud data acquisition framework for intelligent big data analytics for service providers and users. To realize this idea, we propose three main components. The first one is a new global identity provider concept to support fine-grained access control for a federated outsourcing cloud, namely called P-FIPS (Privacy-enhanced Federated Identity Provider System), in which data owners perform identity access control with the operator of the federated outsourcing cloud so that the service providers can selectively use their encrypted data on the cloud for various purpose such as intelligent big data analytics. In P-FIPS, data owners manage the access privilege of service providers over their encrypted data on the cloud by (a) labeling the scope of use (e.g., user connection, user disconnection, user tracking) on each encrypted data on the cloud, and (b) by selectively providing the information regarding the data owners to the service provider. The label also includes the attributes related to the data owner’s identity, and this allows service providers to locate the target data with the assist of cryptographic computation according to the scope of the use at the cloud outsourcing server. The second one is a new ambiguous data acquisition mechanism integrated with P-FIPS from a cloud to a service provider. The last one is the Decentralized Audit and Ordering (DAO) Chain mechanism which provides the correctness of obtained data to the service provider as well as ensures the owners that their data is being used for the approved purpose only. Most importantly, we show that our framework is much more efficient than the existing alternative in the scheme.
Highlights
IoT Analytics is predicted that by 2025, more than 20 billion devices will be connected to the internet [1]
User identifiers can be accumulated through the blockchain and linked to user personal information
To insist on our research objectively, we introduce the privacy policy that GDPR and the global companies consider as follows [29]. (a) Data minimization that allows access to only minimized data using technology (b) Transparency and data management that allows users to check the collected data and make their own selection The key contribution of the proposed research work is summarized as follows. (a) P-FIPS: Users control the use of data by service providers by labeling the scope of use of information
Summary
IoT Analytics is predicted that by 2025, more than 20 billion devices will be connected to the internet [1]. Outsourced servers provide support for data security, and searchable encryption technology has been extensively studied with cloud adoption [12]–[14]. Between the limited computing power of IoT and the user’s data availability, the framework considers the following four perspectives (availability, efficiency, privacy, security) and sub-functions. A. NAIVE SOLUTION Our research goal is to allow access to privacy-enhanced data of attributes by using blockchain and cloud outsourcing. Privacy labeling stores user attributes data in outsourcing servers and allows service providers to access information by calculating cryptography according to user labeling. We want to provide efficiency by applying an outsourced cloud and searchable encryption At this time, users lead privacy labeling and search keywords for it, thereby enhancing privacy [22].
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