Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

In recent years,with the continuous advancement of the intelligent process of the Internet of Vehicles(IoV),the problem of privacy leakage in IoV has become increasingly prominent.The research on the privacy protection of the IoV has become the focus of the society.This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms,proposes a privacy protection system structure based on untrusted data collection server,and designs a vehicle location acquisition algorithm based on a local differential privacy and game model.The algorithm first meshes the road network space.Then,the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model,thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service.On this basis,a statistical method is designed,which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions.Finally,this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai.The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements,and provide better privacy protection and service for the users of the IoV.

DroidEcho:an in-depth dissection of malicious behaviors in Android applications

A precise representation for attacks can benefit the detection of malware in both accuracy and efficiency.However,it is still far from expectation to describe attacks precisely on the Android platform.In addition,new features on Android,such as communication mechanisms,introduce new challenges and difficulties for attack detection.In this paper,we propose abstract attack models to precisely capture the semantics of various Android attacks,which include the corresponding targets,involved behaviors as well as their execution dependency.Meanwhile,we construct a novel graph-based model called the inter-component communication graph(ICCG)to describe the internal control flows and inter-component communications of applications.The models take into account more communication channel with a maximized preservation of their program logics.With the guidance of the attack models,we propose a static searching approach to detect attacks hidden in ICCG.To reduce false positive rate,we introduce an additional dynamic confirmation step to check whether the detected attacks are false alarms.Experiments show that DROIDECHO can detect attacks in both benchmark and real-world applications effectively and efficiently with a precision of 89.5%.