9,975 research outputs found
Privacy Management and Optimal Pricing in People-Centric Sensing
With the emerging sensing technologies such as mobile crowdsensing and
Internet of Things (IoT), people-centric data can be efficiently collected and
used for analytics and optimization purposes. This data is typically required
to develop and render people-centric services. In this paper, we address the
privacy implication, optimal pricing, and bundling of people-centric services.
We first define the inverse correlation between the service quality and privacy
level from data analytics perspectives. We then present the profit maximization
models of selling standalone, complementary, and substitute services.
Specifically, the closed-form solutions of the optimal privacy level and
subscription fee are derived to maximize the gross profit of service providers.
For interrelated people-centric services, we show that cooperation by service
bundling of complementary services is profitable compared to the separate sales
but detrimental for substitutes. We also show that the market value of a
service bundle is correlated with the degree of contingency between the
interrelated services. Finally, we incorporate the profit sharing models from
game theory for dividing the bundling profit among the cooperative service
providers.Comment: 16 page
Emerging privacy challenges and approaches in CAV systems
The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions
Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges
The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy
- …