A Novel Objective Quality Assessment Method for Video Conferencing Coding

Most recently, due to the demand of immersive communication, region-of-interest-based(ROI) high efficiency video coding(HEVC) approaches in conferencing scenarios have become increasingly important. However, there exists no objective metric, specially developed for efficiently evaluating the perceived visual quality of video conferencing coding. Therefore, this paper proposes a novel objective quality assessment method, namely Gaussian mixture model based peak signal-tonoise ratio(GMM-PSNR), for the perceptual video conferencing coding. First, eye tracking experiments, together with a real-time technique of face and facial feature extraction, are introduced. In the experiments, importance of background, face, and facial feature regions is identified, and it is then quantified based on eye fixation points over test videos. Next, assuming that the distribution of the eye fixation points obeys Gaussian mixture model, we utilize expectation-maximization(EM) algorithm to generate an importance weight map for each frame of video conferencing coding, in light of a new term eye fixation points/pixel(efp/p). According to the generated weight map, GMM-PSNR is developed for quality assessment by assigning different weights to the distortion of each pixel in the video frame. Finally, we utilize some experiments to investigate the correlation of the proposed GMM-PSNR and other conventional objective metrics with subjective quality metrics. The experimental results show the effectiveness of GMM-PSNR.

Deeply Understanding Graph-Based Sybil Detection Techniques via Empirical Analysis on Graph Processing

Sybil attacks are one of the most prominent security problems of trust mechanisms in a distributed network with a large number of highly dynamic and heterogeneous devices,which expose serious threat to edge computing based distributed systems.Graphbased Sybil detection approaches extract social structures from target distributed systems,refine the graph via preprocessing methods and capture Sybil nodes based on the specific properties of the refined graph structure.Graph preprocessing is a critical component in such Sybil detection methods,and intuitively,the processing methods will affect the detection performance.Thoroughly understanding the dependency on the graph-processing methods is very important to develop and deploy Sybil detection approaches.In this paper,we design experiments and conduct systematic analysis on graph-based Sybil detection with respect to different graph preprocessing methods on selected network environments.The experiment results disclose the sensitivity caused by different graph transformations on accuracy and robustness of Sybil detection methods.

Lightweight and Manageable Digital Evidence Preservation System on Bitcoin

An effective and secure system used for evidence preservation is essential to possess the properties of anti- loss, anti-forgery, anti-tamper and perfect verifiability. Traditional architecture which relies on centralized cloud storage is depressingly beset by the security problems such as incomplete confidence and unreliable regulation. Moreover, an expensive, inefficient and incompatible design impedes the effort of evidence preservation. In contrast, the decentralized blockchain network is qualified as a perfect replacement for its secure anonymity, irrevocable commitment, and transparent traceability. Combining with subliminal channels in blockchain, we have weaved the transaction network with newly designed evidence audit network. In this paper, we have presented and implemented a lightweight digital evidence-preservation architecture which possesses the features of privacy-anonymity, audit-transparency, function-scalability and operation-lightweight. The anonymity is naturally formed from the cryptographic design, since the cipher evidence under encrypted cryptosystem and hash-based functions leakages nothing to the public. Covert channels are efficiently excavated to optimize the cost, connectivity and security of the framework, transforming the great computation power of Bitcoin network to the value of credit. The transparency used for audit, which relates to the proof of existence, comes from instant timestamps and irreversible hash functions in mature blockchain network. The scalability is represented by the evidence chain interacted with the original blockchain, and the extended chains on top of mainchain will cover the most of auditors in different institutions. And the lightweight, which is equal to low-cost, is derived from our fine-grained hierarchical services. At last, analyses of efficiency, security, and availability have shown the complete accomplishment of our system.

An attack-immune trusted architecture for supervisory aircraft hardware

With the wide application of electronic hardware in aircraft such as air-to-ground communication,satellite communication,positioning system and so on,aircraft hardware is facing great secure pressure.Focusing on the secure problem of aircraft hardware,this paper proposes a supervisory control architecture based on secure System-on-a-Chip(So C)system.The proposed architecture is attack-immune and trustworthy,which can support trusted escrow application and Dynamic Integrity Measurement(DIM)without interference.This architecture is characterized by a Trusted Monitoring System(TMS)hardware isolated from the Main Processor System(MPS),a secure access channel from TMS to the running memory of the MPS,and the channel is unidirectional.Based on this architecture,the DIM program running on TMS is used to measure and call the Lightweight Measurement Agent(LMA)program running on MPS.By this method,the Operating System(OS)kernel,key software and data of the MPS can be dynamically measured without disturbance,which makes it difficult for adversaries to attack through software.Besides,this architecture has been fully verified on FPGA prototype system.Compared with the existing systems,our architecture achieves higher security and is more efficient on DIM,which can fully supervise the running of application and aircraft hardware OS.

Dynamic data auditing scheme for big data storage

When users store data in big data platforms,the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data.However,the existing remote data auditing schemes for big data platforms are only applicable to static data.In order to verify the integrity of dynamic data in a Hadoop big data platform,we presents a dynamic auditing scheme meeting the special requirement of Hadoop.Concretely,a new data structure,namely Data Block Index Table,is designed to support dynamic data operations on HDFS(Hadoop distributed file system),including appending,inserting,deleting,and modifying.Then combined with the MapReduce framework,a dynamic auditing algorithm is designed to audit the data on HDFS concurrently.Analysis shows that the proposed scheme is secure enough to resist forge attack,replace attack and replay attack on big data platform.It is also efficient in both computation and communication.

Physical Unclonable Functions for IoT Device Authentication

The Internet of things(IoT),as an extension of the Internet,has become a trend of network develop-ment nowadays.In order to protect the integrity and authenticity of the information in the IoT,an identity authentication protocol applied to the networked devices is designed in this paper,using the physical unclonable function(PUF)to extract the uniqueness and tamper resistance of the randomness in the manufacturing process of the physical device.We propose the protocol including the database,accessed devices,access devices and users in the specific network environment.Relying on the unique identification information generated by the PUF embedded in devices and passwords set by users,devices and users identities could be verified through zero-knowledge proofs.The performance analysis and the experiment at the end of this work show that our protocol provides users with a strong security guarantee for IoT devices.