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This paper studies how a system operator and a set of agents securely execute a distributed projected gradient-based algorithm. In particular, each participant holds a set of problem coefficients and/or states whose values are private to the data owner. The concerned problem raises two questions: how to securely compute given functions; and which functions should be computed in the first place. For the first question, by using the techniques of homomorphic encryption, we propose novel algorithms which can achieve secure multiparty computation with perfect correctness. For the second question, we identify a class of functions which can be securely computed. The correctness and computational efficiency of the proposed algorithms are verified by two case studies of power systems, one on a demand response problem and the other on an optimal power flow problem.

The drone's open and untrusted environment may create problems for authentication and data sharing. To address this issue, we propose a blockchain-enabled efficient and secure data-sharing model for 5G flying drones. In this model, blockchain and attribute-based encryption (ABE) are applied to ensure the security of instruction issues and data sharing. The authentication mechanism in the model employs a smart contract for authentication and access control, public-key cryptography for providing accounts and ensuring accounts security, and a distributed ledger for security audit. In addition, to speed up outsourced computations and reduce electricity consumption, an ABE model with parallel outsourced computation (ABEM-POC) is constructed, and a generic parallel computation method for ABE is proposed. The analysis of the experimental results shows that parallel computation significantly improves the speed of outsourced encryption and decryption compared with serial computation.

In light of mounting privacy concerns over the increasing collection and use of biometric and behavioral information for travel facilitation, this study examines travelers' online privacy concerns (TOPC) and its impact on willingness to share data with travel providers. A proposed theoretical model explaining antecedents and outcomes of TOPC related to biometric and behavioral data sharing was tested using structural equation modeling with data collected from 685 travelers. The results extend the Antecedents -Privacy Concerns -Outcomes (APCO) framework by identifying a set of salient individual factors that shape TOPC. The findings provide empirical evidence confirming the context dependence of privacy preferences, showing that although travelers are concerned over their information privacy they are still willing to share their behavioral data; while in the case of biometric information, the disclosure decision is dependent upon expected benefits rather than privacy concerns. This study offers insights into privacy behavior of online consumers in the travel context and constitutes one of the few focusing on the social aspects of biometric authentication.

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