"If You Can't Beat them, Join them": A Usability Approach to Interdependent Privacy in Cloud Apps

Cloud storage services, like Dropbox and Google Drive, have growing ecosystems of 3rd party apps that are designed to work with users' cloud files. Such apps often request full access to users' files, including files shared with collaborators. Hence, whenever a user grants access to a new vendor, she is inflicting a privacy loss on herself and on her collaborators too. Based on analyzing a real dataset of 183 Google Drive users and 131 third party apps, we discover that collaborators inflict a privacy loss which is at least 39% higher than what users themselves cause. We take a step toward minimizing this loss by introducing the concept of History-based decisions. Simply put, users are informed at decision time about the vendors which have been previously granted access to their data. Thus, they can reduce their privacy loss by not installing apps from new vendors whenever possible. Next, we realize this concept by introducing a new privacy indicator, which can be integrated within the cloud apps' authorization interface. Via a web experiment with 141 participants recruited from CrowdFlower, we show that our privacy indicator can significantly increase the user's likelihood of choosing the app that minimizes her privacy loss. Finally, we explore the network effect of History-based decisions via a simulation on top of large collaboration networks. We demonstrate that adopting such a decision-making process is capable of reducing the growth of users' privacy loss by 70% in a Google Drive-based network and by 40% in an author collaboration network. This is despite the fact that we neither assume that users cooperate nor that they exhibit altruistic behavior. To our knowledge, our work is the first to provide quantifiable evidence of the privacy risk that collaborators pose in cloud apps. We are also the first to mitigate this problem via a usable privacy approach.Comment: Authors' extended version of the paper published at CODASPY 201

Blockchain-based Cloud Data Deduplication Scheme with Fair Incentives

With the rapid development of cloud computing, vast amounts of duplicated data are being uploaded to the cloud, wasting storage resources. Deduplication (dedup) is an efficient solution to save storage costs of cloud storage providers (CSPs) by storing only one copy of the uploaded data. However, cloud users do not benefit directly from dedup and may be reluctant to dedup their data. To motivate the cloud users towards dedup, CSPs offer incentives on storage fees. The problems with the existing dedup schemes are that they do not consider: (1) correctness - the incentive offered to a cloud user should be computed correctly without any prejudice. (2) fairness - the cloud user receives the file link and access rights of the uploaded data if and only if the CSP receives the storage fee. Meeting these requirements without a trusted party is non-trivial, and most of the existing dedup schemes do not apply. Another drawback is that most of the existing schemes emphasize incentives to cloud users but failed to provide a reliable incentive mechanism. As public Blockchain networks emulate the properties of trusted parties, in this paper, we propose a new Blockchain-based dedup scheme to meet the above requirements. In our scheme, a smart contract computes the incentives on storage fee, and the fairness rules are encoded into the smart contract for facilitating fair payments between the CSPs and cloud users. We prove the correctness and fairness of the proposed scheme. We also design a new incentive mechanism and show that the scheme is individually rational and incentive compatible. Furthermore, we conduct experiments by implementing the designed smart contract on Ethereum local Blockchain network and list the transactional and financial costs of interacting with the designed smart contract

When Mobile Blockchain Meets Edge Computing

Blockchain, as the backbone technology of the current popular Bitcoin digital currency, has become a promising decentralized data management framework. Although blockchain has been widely adopted in many applications, e.g., finance, healthcare, and logistics, its application in mobile services is still limited. This is due to the fact that blockchain users need to solve preset proof-of-work puzzles to add new data, i.e., a block, to the blockchain. Solving the proof-of-work, however, consumes substantial resources in terms of CPU time and energy, which is not suitable for resource-limited mobile devices. To facilitate blockchain applications in future mobile Internet of Things systems, multiple access mobile edge computing appears to be an auspicious solution to solve the proof-of-work puzzles for mobile users. We first introduce a novel concept of edge computing for mobile blockchain. Then, we introduce an economic approach for edge computing resource management. Moreover, a prototype of mobile edge computing enabled blockchain systems is presented with experimental results to justify the proposed concept.Comment: Accepted by IEEE Communications Magazin