Xianghan Zheng

Detecting spammers on social networks

Social network has become a very popular way for internet users to communicate and interact online. Users spend plenty of time on famous social networks (e.g., Facebook, Twitter, Sina Weibo, etc.), reading news, discussing events and posting messages. Unfortunately, this popularity also attracts a significant amount of spammers who continuously expose malicious behavior (e.g., post messages containing commercial URLs, following a larger amount of users, etc.), leading to great misunderstanding and inconvenience on users? social activities. In this paper, a supervised machine learning based solution is proposed for an effective spammer detection. The main procedure of the work is: first, collect a dataset from Sina Weibo including 30,116 users and more than 16 million messages. Then, construct a labeled dataset of users and manually classify users into spammers and non-spammers. Afterwards, extract a set of feature from message content and users? social behavior, and apply into SVM (Support Vector Machines) based spammer detection algorithm. The experiment shows that the proposed solution is capable to provide excellent performance with true positive rate of spammers and non-spammers reaching 99.1% and 99.9% respectively.

Lightweight distributed secure data management system for health internet of things

Internet of Things (IoT) connects various kinds of sensors and smart devices using the internet to collect data. The adoption of IoT in medical care field will bring great convenient to both doctors and patients for effective illness monitoring and diagnosis. Due to the high value of medical data and the openness character of health IoT, the protection of data confidentiality is of crucial importance. In this paper, we propose a novel distributed secure data management with keyword search system for health IoT. Since the patients are usually managed by diverse medical institutions, the proposed system enables distributed access control of protected health information (PHI) among different medical domains. On the other hand, the accumulation of electronic health records (EHR) makes effective data retrieval a challenge task. Our scheme could provide efficient keyword search function on cross-domain PHI. For the resource limited devices in health IoT, it is an essential requirement to design lightweight algorithms in the secure data management system. The proposed system realizes lightweight data encryption, lightweight keyword trapdoor generation and lightweight data recovery, which leaves very few computations to users terminal. The security of this system is reduced to the decisional bilinear Diffie-Hellman (DBDH) assumption. The comparison analysis is made between this scheme and other existing systems. The extensive experiments on both laptop and smart phone platforms show that the proposed scheme has greatly improved the computation efficiency and requires much less communication cost.

A Cloud-based monitoring framework for Smart Home

Today, Smart Home monitoring services have attracted much attention from both academia and industry. However, in the conventional monitoring mechanism the remote camera can not be accessed for remote monitoring anywhere and anytime. Besides, traditional approaches might have the limitation in local storage due to lack of device elasticity. In this paper, we proposed a Cloud-based monitoring framework to implement the remote monitoring services of Smart Home. The main technical issues considered include Data-Cloud storage, Local-Cache mechanism, Media device control, NAT traversal, etc. The implementation shows three use scenarios: (a) operating and controlling video cameras for remote monitoring through mobile devices or sound sensors; (b) streaming live video from cameras and sending captured image to mobile devices; (c) recording videos and images on a cloud computing platform for future playback. This system framework could be extended to other applications of Smart Home.

Mobile Cloud Based Framework for Remote-Resident Multimedia Discovery and Access

Due to realistic barriers (e.g., network heterogeneity, NAT traversal, limited computing/storage, etc.), a majority of legacy devices (especially mobile devices) are difficult to discover and access residential services from remote environment. In this paper, we propose a mobile cloud based architecture for enabling remote-resident multimedia service discovery and access. The main considered issues in the paper includes: security, cloud-based storage, cloud-based remote service discovery and control, REST-based lightweight negotiation, etc. After that, AHP and fuzzy TOPSIS algorithms are combined for cloud candidate selection optimization. Prototype implementation and theoretical analysis show availability and efficiency of proposed approaches.

Lattice assumption based fuzzy information retrieval scheme support multi-user for secure multimedia cloud

Multimedia cloud is novel computation paradigm which could leverage cloud infrastructure to store large quantity of multimedia documents and respond on the requests from customers. With the development of multimedia cloud, an increasing attention is paid to its privacy and security issues. Searchable encryption (SE) technology could protect the sensitive information of cloud storage data and at the same time allow keyword search query. Most of the available SE schemes are constructed using the bilinear map. However, both discrete logarithms and factorization are proved to be solved by quantum computer in a polynomial time. Thus, those schemes are not secure in quantum age. Moreover, majority SE schemes are limited in exact or fuzzy keyword search. They can not support the semantically keyword equivalent judgement. In order to solve those problems, we suggest a novel data retrieval scheme for multiple users based on the lattice based mechanism. The contribution of this paper is summarized in three aspects: lattice assumption based scheme to resist quantum attack, semantically keyword search to enable synonym query and broadcast encryption based mechanism to support multiple user system without sharing secret key. This scheme is a candidate for secure multimedia cloud even in quantum-era since the LWE problem is secure against quantum attack.

Privacy-preserving smart IoT-based healthcare big data storage and self-adaptive access control system

Abstract In this paper, a privacy-preserving smart IoT-based healthcare big data storage system with self-adaptive access control is proposed. The aim is to ensure the security of patients’ healthcare data, realize access control for normal and emergency scenarios, and support smart deduplication to save the storage space in big data storage system. The medical files generated by the healthcare IoT network are encrypted and transferred to the storage system, which can be securely shared among the healthcare staff from different medical domains leveraging a cross-domain access control policy. The traditional access control technology allows the authorized data users to decrypt patient’s sensitive medical data, but also hampers the first-aid treatment when the patient’s life is threatened because the on-site first-aid personnel are not permitted to get patient’s historical medical data. To deal with this dilemma, we propose a secure system to devise a novel two-fold access control mechanism, which is self-adaptive for both normal and emergency situations. In normal application, the healthcare staff with proper attribute secret keys can have the data access privilege; in emergency application, patient’s historical medical data can be recovered using a password-based break-glass access mechanism. To save the storage overhead in the big data storage system, a secure deduplication method is designed to eliminate the duplicate medical files with identical data, which may be encrypted with different access policies. A highlight of this smart secure deduplication method is that the remaining medical file after the deduplication can be accessed by all the data users authorized by the different original access policies. This smart healthcare big data storage system is formally proved secure, and extensive comparison and simulations demonstrate its efficiency.

Architecture-based integrated management of diverse cloud resources

Cloud management faces with great challenges, due to the diversity of Cloud resources and ever-changing management requirements. For constructing a management system to satisfy a specific management requirement, a redevelopment solution based on existing management systems is usually more practicable than developing the system from scratch. However, the difficulty and workload of redevelopment are also very high. As the architecture-based runtime model is causally connected with the corresponding running system automatically, constructing an integrated Cloud management system based on the architecture-based runtime models of Cloud resources can benefit from the model-specific natures, and thus reduce the development workload. In this paper, we present an architecture-based approach to managing diverse Cloud resources. First, manageability of Cloud resources is abstracted as runtime models, which could automatically and immediately propagate any observable runtime changes of target resources to corresponding architecture models, and vice versa. Second, a customized model is constructed according to the personalized management requirement and the synchronization between the customized model and Cloud resource runtime models is ensured through model transformation. Thus, all the management tasks could be carried out through executing programs on the customized model. The experiment on a real-world cloud demonstrates the feasibility, effectiveness and benefits of the new approach to integrated management of Cloud resources

Survey on Software-Defined Networking

Recently, both the academia and industry have initiated research directed toward the integration of software-defined networking SDN technologies into the next generation of networking. In this paradigm, SDN transfers the control function from the traditional distributed network equipment to the controllable computing devices, which makes the underlying network infrastructure abstract to network services and applications. In this study, we survey OpenFlow-based SDN solutions that were recently proposed in both academia and industry. We consider technical issues, including SDN requirement, OpenFlow-based approach, challenges, and possible approaches. In addition, security breaches and possible solutions are described. Our survey is based on recent research publications.

A System Architecture for Intelligent Logistics System

Conventional logistics information systems are usually designed case by case without consideration of many issues in fields of system elasticity, real time tracking service, information sharing and inter-working, vehicle routing optimization, customer interaction, security control, etc. In this paper, we investigate and propose a mobile cloud-based architecture for enrichment of existing logistics systems. The system architecture includes: customer interaction, distributed storage and computing, vehicle scheduling, restful-based interaction and transmission, security, mobile payment, etc. After that, prototype implementation and evaluation work are presented to show feasibility and efficiency of proposed solution.

Interest prediction in social networks based on Markov chain modeling on clustered users

Effective user interest prediction is significant for service providers in a set of application scenarios such as user behavior analysis and resource recommendation. However, existing approaches are either incomplete or proprietary. In this paper, user interest prediction based on the Markov chain modeling on clustered users is proposed with the following procedure: collect dataset from 4613 users and more than 16 million messages from Sina Weibo; obtain each users interest eigenvalue sequence and establish single‐Markov chain model; and implement user clustering algorithm for the multi‐Markov chain construction in order to divide users into a set of predefined interest categories. The proposed solution is capable of predicting both long‐term and short‐term user interests based on a suitable selection of the initial state distribution, λ. The proposed solution also proves that short‐term interests are consistent with long‐term interests if the influences of social or user‐related events that cause interruptions (e.g., earthquake and birthday) are not considered. Furthermore, experiments show that the proposed solution is feasible and efficient and can achieve a higher accuracy of prediction than that of the other approaches such as Support Vector Machine (SVM) and K‐means. Copyright