Jinghua Jiang

Enabling Encrypted Cloud Media Center with Secure Deduplication

Multimedia contents, especially videos, are being exponentially generated today. Due to the limited local storage, people are willing to store the videos at the remote cloud media center for its low cost and scalable storage. However, videos may have to be encrypted before outsourcing for privacy concerns. For practical purposes, the cloud media center should also provide the deduplication functionality to eliminate the storage and bandwidth redundancy, and adaptively disseminate videos to heterogeneous networks and different devices to ensure the quality of service. In light of the observations, we present a secure architecture enabling the encrypted cloud media center. It builds on top of latest advancements on secure deduplication and video coding techniques, with fully functional system implementations on encrypted video deduplication and adaptive video dissemination services. Specifically, to support efficient adaptive dissemination, we utilize the scalable video coding (SVC) techniques and propose a tailored layer-level secure deduplication strategy to be compatible with the internal structure of SVC. Accordingly, we adopt a structure-compatible encryption mechanism and optimize the way how encrypted SVC videos are stored for fast retrieval and efficient dissemination. We thoroughly analyze the security strength of our system design with strong video protection. Furthermore, we give a prototype implementation with encrypted end-to-end deployment on Amazon cloud platform. Extensive experiments demonstrate the practicality of our system.

Toward Encrypted Cloud Media Center With Secure Deduplication

The explosive growth of multimedia contents, especially videos, is pushing forward the paradigm of cloud-based media hosting today. However, the wide attacking surface of the public cloud and the growing security awareness from the society are both calling for data encryption before outsourcing to cloud. Under the circumstance of encrypted videos, how to still preserve all the service benefits of cloud media center remains to be fully explored. In this paper, we present a secure system architecture design as our initial effort toward this direction, which bridges together the advancements of video coding techniques and secure deduplication. Our design enables the cloud with the crucial deduplication functionality to completely eliminate the extra storage and bandwidth cost, which would have been incurred by hosting encrypted videos from different entities. The design is also carefully tailored to the scalable video coding (SVC) techniques to support heterogeneous networks and devices for high-quality adaptive video dissemination. We show fully functional system implementations with structure-aware encryption design and structure-aware deduplication strategies that are both completely compliant with the video format in SVC. Extensive security analysis and experiments via our prototype deployed on Azure cloud platform show the practicality of the design. Our work can also be easily extended to support other media applications that employ media files with scalable structures.

Towards Secure and Accurate Targeted Mobile Coupon Delivery

This paper presents our research on secure and accurate targeted mobile coupon delivery. Our goal is to enable the secure delivery of targeted coupons to eligible users equipped with mobile devices, whose behavioral profiles accurately satisfy the targeting profile defined by the vendor. Our design well preserves user privacy, and further provides the strict security guarantee of vendor protection, by verifying users eligibility for a coupon without revealing the vendors targeting profile. We first show a basic approach which can effectively address the challenges posed by secure and accurate targeted coupon delivery, via properly leveraging Yaos garbled circuits. In order to achieve practical performance for resource-limited mobile devices, we then present our proposed design, which imposes lightweight workload on the user side, via properly bridging together homomorphic encryption and Yaos garbled circuits. We implement a preliminary user-side prototype and deploy it on an Android smartphone to evaluate the performance. Extensive experimental results demonstrate that our proposed design achieves practical performance for mobile devices.

Social Relation Cognitive Model of Mobile Nodes in the Internet of Things

In the Internet of Things,the quantification of social relations is the basis of mobile-aware service,which involves many decision factors,such as time,space and behavior.Based on social network theory,a new cognitive model for mobile nodes social relation in the Internet of Things is proposed.Firstly,by reasoning and evaluating the complexity and uncertainty of social relation from various angles,the social characteristics of mobile nodes are summarized.Then,we would quantize the social relations of mobile nodes using location factor,interconnection factor,service quality factor and feedback aggregation factor,so as to solve the shortcomings in these quantitative models which are caused by the single decision factors and the limitation of the calculated results.Finally,the weight distribution is set up by information entropy and rough set theory for these decision factors,which overcomes the shortage of traditional method,in which the weight is set up by subjective manners and has poor dynamic adaptability.Simulation results show that,cognitive model has better predictive accuracy and dynamic adaptability.

Location Attraction Mobility Model for Mobile Ad Hoc Network

Mobility models play a crucial role in mobility wireless networks with respect to evaluating the network protocols and performances. However, the majority of existing mobility models either does not exhibit realistic movement characteristics or modeling methods are too complex. In this paper, a new mobility model based on Location Attraction (LAMM) is proposed, which utilizes humans clustering features in real life. Through analyzing real GPS trace of mobile users, we observe the number and location attraction of hot regions. We find that the number of hot regions is extremely stationary in an observation area, and the pause position density within hot regions show a trend with exponential decline. Based on such movement characteristics, we developed a mobility model and the validation shows that LAMM better depict the mobility patterns of human.

A Practical System for Privacy-Aware Targeted Mobile Advertising Services

With the prosperity of mobile application markets, mobile advertising is becoming an increasingly important economic force. In order to maximize revenue, ads are recommended to be delivered to potentially interested users, which requires user targeting, i.e., analyzing users’ profiles and exploring users’ interests. However, collecting user personal information for targeted mobile advertising services raises critical privacy concerns. Although some solutions like anonymization and obfuscation have been proposed for privacy-aware targeted advertising, they undesirably face the issues of security, efficiency, and/or ad relevance. In this paper, we propose a practical system enabling secure and efficient targeted mobile advertising services. It allows the ad network to perform accurate user targeting, while ensuring strong privacy protection for mobile users. Specifically, we show how to properly leverage a cryptographic primitive called private stream searching to support secure, accurate, and practical targeted mobile ad delivery. Moreover, we propose secure billing schemes to enable the ad network to charge advertisers in a privacy-preserving manner. The security strength of our system is thoroughly analyzed. Through extensive experiments, we show that our system achieves practical efficiency on mobile devices.

Towards privacy-preserving user targeting

User targeting via behavioral analysis is becoming increasingly prevalent in online messaging services. By taking into account users’ behavior information such as geographic locations, purchase behaviors, and search histories, vendors can deliver messages to users who are more likely to have a strong preference. For example, advertisers can rely on some ad-network for distributing ads to targeted users. However, collecting such personal information for accurate targeting raises severe privacy concerns. In order to incentivize users to participate in such behavioral targeting systems, addressing the privacy concerns becomes of paramount importance. We provide a survey of privacy-preserving user targeting. We present the architectures of user targeting, the security threats faced by user targeting, and existing approaches to privacy-preserving user targeting. Some future research directions are also identified.