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Wireless Communications and Mobile Computing
Volume 2018, Article ID 8281379, 2 pages

Privacy in the Internet of Things

1Department of Computer Science, Georgia State University, Atlanta, GA, USA
2IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
3Mid Sweden University, Sundsvall, Sweden
4University of Ljubljana, Ljubljana, Slovenia
5Tsinghua University, Beijing, China

Correspondence should be addressed to Zhipeng Cai; ude.usg@iacz

Received 20 November 2017; Accepted 21 November 2017; Published 11 April 2018

Copyright © 2018 Zhipeng Cai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The ubiquity of sensor devices in Internet of Things opens up a large source for sensory data, which has shown great potential in benefiting the industry as well as improving people’s quality of life. Sensory data has become an indispensable component in wide variety of applications, including manufacturing, marketing, healthcare, transportation, entertainment, environmental monitoring, indoor localization, and traffic monitoring, and will continue to play an even more pivotal role in the near future. Smart sensors are being employed in more and more manufacturers. Thanks to sensory data, their efficiency has been substantially increased, product defects have been dramatically reduced, and customers’ quality of experience has been improved to a maximum extent. It is worth mentioning that more than 40 percent of data generated from Internet of Things come from sensors. On the other hand, the size of sensory data has already overwhelmed the current ability to collect, store, and analyze them, which is one of the major bottlenecks to the further development of IoT applications. More problematically, consumers’ privacy is being seriously threatened by the huge amount of sensory data revealing personal information. Unfortunately, conventional privacy protection methodologies are mainly for small-scale or isomorphic data, and they are not effective or efficient for big sensory data. Therefore, corresponding privacy protection technologies and tools are eagerly expected so that people can enjoy the benefits of sensory data with privacy being preserved anytime and anywhere.

This special issue solicits high-quality contributions that focus on designing new technologies and tools to address the privacy issues towards sensory data.

Due to the great effort made in publicity, we received 16 submissions from both academia and industry in the relevant fields. Following a strict review process, we accepted 10 papers for this special issue. Each of the papers was peer-reviewed by at least two experts in the field. In the following, we provide a brief introduction to each paper.

In the paper titled “An Advanced Private Social Activity Invitation Framework with Friendship Protection,” the authors proposed a social activity invitation framework with privacy guarantee. In the proposed framework, it is assumed that a server can help with organizing group activities intelligently and efficiently even though it is untrustworthy. The authors also introduce a new definition for friendship so that a social network can be modeled as a directed graph.

The work in the paper titled “A Location Prediction-Based Helper Selection Scheme for Suspicious Eavesdroppers” aims to improve security performance of data transmission by a mobile eavesdropper in a wireless network. A location-based prediction scheme is proposed to predict the eavesdroppers location and to decide whether a friendly jamming measure should be selected against the eavesdropper. The proposed scheme can help with saving system power.

The authors of the paper titled “Why You Go Reveals Who You Know: Disclosing Social Relationship by Cooccurrence” study two issues, which are “how to distinguish cooccurrences between acquaintances and strangers” and “what kind of cooccurrence contributes to strong social strength.” To address these two issues, a social relationship attack model is introduced and the mobility intention-based relationship inference model is proposed. The experimental results show that the proposed model can properly differentiate between cooccurrences.

To defend against password inference attacks, the work in the paper titled “Privacy Enhancing Keyboard: Design, Implementation, and Usability Testing” introduces a context-aware privacy-enhancing keyboard (PEK) for Android touch-based devices. When a user enters a password, a keyboard is shown with the positions of the characters randomly shuffled. PEK has been released in Google Play since 2014.

The paper titled “PBF: A New Privacy-Aware Billing Framework for Online Electric Vehicles with Bidirectional Auditability” focuses on online electric vehicle (OLEV), where vehicles are propelled by wirelessly transmitted electrical power while moving. The authors present a secure and privacy-aware fair billing framework for OLEV. Two extreme lightweight mutual authentication mechanisms are proposed. Furthermore, a secure and privacy-aware wireless power transfer method is introduced. This work guarantees secure, privacy-aware, and fair billing for OLEVs.

In the paper titled “A Secure and Scalable Data Communication Scheme in Smart Grids,” the authors present communication architecture for smart grids and propose a scheme to guarantee security and privacy of data communications among smart meters, utility companies, and data repositories. The proposed architecture employs an access control Linear Secret Sharing Scheme matrix to achieve role-based access control.

The work in the paper titled “Scalable and Soundness Verifiable Outsourcing Computation in Marine Mobile Computing” proposes a scalable and verifiable outsourcing computation protocol (SV-OC) in marine cloud computing. A single-mode version (SM-SV-OC) is then introduced. Both protocols allow anyone who holds verification tokens to efficiently verify the computed result returned from cloud.

The authors of the paper titled “Trust on the Ratee: A Trust Management System for Social Internet of Vehicles” study trust management in Social Internet of Vehicles (SIoV), which enables vehicles to establish social relationships autonomously to improve traffic conditions. A Ratee-based Trust Management (RTM) system is proposed. RTM is built based on SIoV so that node relationships can increase the accuracy of trustworthiness.

In the paper titled “Security Enhancement for Multicast over Internet of Things by Dynamically Constructed Fountain Codes,” the authors investigate the security challenge of multicast applications in IoT. Considering the existence of eavesdropper, an adaptive fountain code design is proposed to enhance security for multicast in IoT. The proposed scheme is a dynamic encoding scheme that can effectively decrease intercept probability at the eavesdropper and can increase transmission.

We would like to thank all the authors for their great contributions to this special issue. We would also like to thank all anonymous reviewers for their valuable comments which help the authors to further improve the papers. It is an honor for all of us to serve as Guest Editors at Wireless Communications and Mobile Computing.

Zhipeng Cai
Rong N. Chang
Stefan Forsström
Anton Kos
Chaokun Wang