Privacy Computing: Concept, Computing Framework, and Future Development Trends

With the rapid development of information technology and the continuous evolution of personalized ser- vices, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private infor- mation in different information systems. However, problems such as the cask principle of preserving pri- vate information among different information systems and the dif culty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot pro- vide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a the- ory of privacy computing and a key technology system that includes a privacy computing framework, a formal de nition of privacy computing, four principles that should be followed in privacy computing, ffect algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.

Privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

The widespread use of Location-Based Services （LBSs）, which allows untrusted service providers to collect large quantities of information regarding users＇ locations, has raised serious privacy concerns. In response to these issues, a variety of LBS Privacy Protection Mechanisms （LPPMs） have been recently proposed. However, evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics. In particular, the relationships between these metrics have not been examined in depth under a common adversarial model, leading to a possible selection of the inappropriate metric, which runs the risk of wrongly evaluating LPPMs. In this paper, we address these issues by proposing a privacy quantification model, which is based on Bayes conditional privacy, to specify a general adversarial model. This model employs a general definition of conditional privacy regarding the adversary＇s estimation error to compare the different LBS privacy metrics. Moreover, we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics. We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective. Our results contribute to a better understanding of how privacy properties can be measured, as well as to the better selection of the most appropriate metric for any given LBS application.

Privacy-friendly spatial crowdsourcing in vehic-ular networks

With the evolution of conventional VANETs(Vehicle Ad-hoc Networks)into the IoV(Internet of Vehicles),vehicle-based spatial crowdsourcing has become a potential solution for crowdsourcing applications.In vehicular networks,a spatial-temporal task/question can be outsourced(i.e.,task/question relating to a particular location and in a speci c time period)to some suitable smart vehicles(also known as workers)and then these workers can help solve the task/question.However,an inevitable barrier to the widespread deployment of spatial crowdsourcing applications in vehicular networks is the concern of privacy.Hence,We propose a novel privacy-friendly spatial crowdsourcing scheme.Unlike the existing schemes,the proposed scheme considers the privacy issue from a new perspective according that the spatial-temporal tasks can be linked and analyzed to break the location privacy of workers.Speci cally,to address the challenge,three privacy requirements(i.e.anonymity,untraceability,and unlinkability)are de ned and the proposed scheme combines an effcient anonymous technique with a new composite privacy metric to protect against attackers.Detailed privacy analyses show that the proposed scheme is privacy-friendly.In addition,performance evaluations via extensive simulations are also conducted,and the results demonstrate the effciency and e ectiveness of the proposed scheme.

Background knowledge based privacy metric model for online social networks

The data of online social network （OSN） is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces （APIs） and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from weibo.com has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above