C3P: Context-Aware Crowdsourced Cloud Privacy

Abstract

Abstract. Due to the abundance of attractive services available on the cloud, people are placing an increasing amount of their data online on different cloud platforms. However, given the recent large-scale attacks on users data, privacy has become an important issue. Ordinary users cannot be expected to manually specify which of their data is sensitive, or to take appropriate measures to protect such data. Furthermore, usually most people are not aware of the privacy risk that different shared data items can pose. In this paper, we present a novel conceptual framework in which privacy risk is automatically calculated using the sharing context of data items. To overcome ignorance of privacy risk on the part of most users, we use a crowdsourcing based approach. We use Item Response Theory (IRT) on top of this crowdsourced data to determine the sensitivity of items and diverse attitudes of users towards privacy. First, we determine the feasibility of IRT for the cloud scenario by asking workers feedback on Amazon mTurk on various sharing scenarios. We obtain a good fit of the responses with the theory, and thus show that IRT, a well-known psychometric model for educational purposes, can be applied to the cloud scenario. Then, we present a lightweight mechanism such that users can crowdsource their sharing contexts with the server and determine the risk of sharing particular data item(s) privately. Finally, we use the Enron dataset to simulate our conceptual framework and also provide experimental results using synthetic data. We show that our scheme converges quickly and provides accurate privacy risk scores under varying conditions.

Emerging Privacy Issues and Solutions in Cyber-Enabled Sharing Services: From Multiple Perspectives

“…The top priority for privacy preservation in crowdtesting is to protect user privacy in the data collection process. Harkous et al [56] found that users usually had difficulties in accessing the privacy levels of their shared data. A context-aware framework was proposed to identify the privacy risk of shared data on a cloud server.…”

“…The main limitation of their work is that the proposed system only identifies the risky data items without proposing solutions. Moreover, there is no policy or computational technique proposed in [56].…”

“…Although the crowdtesting service provides an innovative way for services/products to be tested by a large group of testers at low costs, the privacy issues were never well addressed to protect the sensitive information from both the requesters and the testers. Three specific applications of the crowdtesting practices are surveyed in this subsection: shared data protection on the cloud servers [56], online surveys [57] and indoor site survey practice [59]. The objectives, solutions and main insufficiencies are listed in Table III.…”

“…However, the surveyed works suggest that a certain level of privacy sacrifice can be helpful in crowdfunding practice. For crowdtesting practices, three real-world applications were surveyed, including shared data protection on a the cloud server [56], online surveys [57] and indoor site survey practice [59]. The main difficulties in protecting the privacy in crowdtesting practices are identified, which leads to one of the future research directions in the crowdtesting field.…”

StoreSim: Optimizing Information Leakage in Multicloud Storage Services

“…Many studies [20,21,22] focus on security and privacy aspects which are major obstacles in cloud adoption for both individuals and companies. Previous work [21] proposed a semantic framework based on crowd-sourcing to determine the sensitivity of items and diverse attitudes of users towards privacy. Bohli et al [20] provide a survey for four different multicloud architectures with various security and privacy-enhancing designs.…”