Surya Nepal

A survey of trust in social networks

Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. In addition to individuals using such networks to connect to their friends and families, governments and enterprises have started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the level of trust that members have with each other as well as with the service provider. Therefore, trust becomes an essential and important element of a successful social network. In this article, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. We then discuss recent works addressing three aspects of social trust: trust information collection, trust evaluation, and trust dissemination. Finally, we compare and contrast the literature and identify areas for further research in social trust.

Automatic detection of 'Goal' segments in basketball videos

Advances in the media and entertainment industries, for example streaming audio and digital TV, present new challenges for managing large audio-visual collections. Efficient and effective retrieval from large content collections forms an important component of the business models for content holders and this is driving a need for research in audio-visual search and retrieval. Current content management systems support retrieval using low-level features, such as motion, colour, texture, beat and loudness. However, low-level features often have little meaning for the human users of these systems, who much prefer to identify content using high-level semantic descriptions or concepts. This creates a gap between the system and the user that must be bridged for these systems to be used effectively. The research presented in this paper describes our approach to bridging this gap in a specific content domain, sports video. Our approach is based on a number of automatic techniques for feature detection used in combination with heuristic rules determined through manual observations of sports footage. This has led to a set of models for interesting sporting events-goal segments-that have been implemented as part of an information retrieval system. The paper also presents results comparing output of the system against manually identified goals.

A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Data Sets in Cloud

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.

A platform for secure monitoring and sharing of generic health data in the Cloud

The growing need for the remote caring of patients at home combined with the ever-increasing popularity of mobile devices due to their ubiquitous nature has resulted in many apps being developed to enable mobile telecare. The Cloud, in combination with mobile technologies has enabled doctors to conveniently monitor and assess a patients health while the patient is at the comfort of their own home. This demands sharing of health information between healthcare teams such as doctors and nurses in order to provide better and safer care of patients. However, the sharing of health information introduces privacy and security issues which may conflict with HIPAA standards. In this paper, we attempt to address the issues of privacy and security in the domain of mobile telecare and Cloud computing. We first demonstrate a telecare application that will allow doctors to remotely monitor patients via the Cloud. We then use this system as a basis to showcase our model that will allow patients to share their health information with other doctors, nurses or medical professional in a secure and confidential manner. The key features of our model include the ability to handle large data sizes and efficient user revocation.

IoT middleware : a survey on issues and enabling technologies

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

A hybrid approach for scalable sub-tree anonymization over big data using MapReduce on cloud

In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services. Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation. Top–Down Specialization (TDS) and Bottom–Up Generalization (BUG) are two ways to fulfill sub-tree anonymization. However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud. Still, either TDS or BUG individually suffers from poor performance for certain valuing of k-anonymity parameter. In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data. Further, we design MapReduce algorithms for the two components (TDS and BUG) to gain high scalability. Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches.

An efficient quasi-identifier index based approach for privacy preservation over incremental data sets on cloud

Cloud computing provides massive computation power and storage capacity which enable users to deploy applications without infrastructure investment. Many privacy-sensitive applications like health services are built on cloud for economic benefits and operational convenience. Usually, data sets in these applications are anonymized to ensure data owners? privacy, but the privacy requirements can be potentially violated when new data join over time. Most existing approaches address this problem via re-anonymizing all data sets from scratch after update or via anonymizing the new data incrementally according to the already anonymized data sets. However, privacy preservation over incremental data sets is still challenging in the context of cloud because most data sets are of huge volume and distributed across multiple storage nodes. Existing approaches suffer from poor scalability and inefficiency because they are centralized and access all data frequently when update occurs. In this paper, we propose an efficient quasi-identifier index based approach to ensure privacy preservation and achieve high data utility over incremental and distributed data sets on cloud. Quasi-identifiers, which represent the groups of anonymized data, are indexed for efficiency. An algorithm is designed to fulfil our approach accordingly. Evaluation results demonstrate that with our approach, the efficiency of privacy preservation on large-volume incremental data sets can be improved significantly over existing approaches. Highlights? We investigate the efficiency of privacy preservation over large-scale incremental data sets on cloud. ? A distributed quasi-identifier index structure is proposed to efficiently update anonymous incremental data sets. ? Locality-sensitive hash function is used to place similar quasi-identifier groups on the same node to improve efficiency. ? We elaborate the process of generalization or specialization when data updates occur.

A Time Efficient Approach for Detecting Errors in Big Sensor Data on Cloud

Big sensor data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity it is difficult to process using on-hand database management tools or traditional data processing applications. Cloud computing provides a promising platform to support the addressing of this challenge as it provides a flexible stack of massive computing, storage, and software services in a scalable manner at low cost. Some techniques have been developed in recent years for processing sensor data on cloud, such as sensor-cloud. However, these techniques do not provide efficient support on fast detection and locating of errors in big sensor data sets. For fast data error detection in big sensor data sets, in this paper, we develop a novel data error detection approach which exploits the full computation potential of cloud platform and the network feature of WSN. Firstly, a set of sensor data error types are classified and defined. Based on that classification, the network feature of a clustered WSN is introduced and analyzed to support fast error detection and location. Specifically, in our proposed approach, the error detection is based on the scale-free network topology and most of detection operations can be conducted in limited temporal or spatial data blocks instead of a whole big data set. Hence the detection and location process can be dramatically accelerated. Furthermore, the detection and location tasks can be distributed to cloud platform to fully exploit the computation power and massive storage. Through the experiment on our cloud computing platform of U-Cloud, it is demonstrated that our proposed approach can significantly reduce the time for error detection and location in big data sets generated by large scale sensor network systems with acceptable error detecting accuracy.

STrust: A Trust Model for Social Networks

We propose a trust model for social networks with the aim of building trust communities that inspire members to share their experiences, feelings and opinions in an open and honest way without the fear of being judged. The unique feature of our model is that the trust value is derived from the social capital built in the social networks over a period of time. First, we introduce a framework for building trust communities using social trust. We then define an underlying social trust model, called STrust. Finally, we report the current state of the development and the analysis of the proposed trust model.

DIaaS: Data Integrity as a Service in the Cloud

In this paper, we propose a secure cloud storage service architecture with the focus on Data Integrity as a Service (DIaaS) based on the principles of Service-Oriented Architecture and Web services. Our approach not only releases the burdens of data integrity management from a storage service by handling it through an independent third party data Integrity Management Service (IMS), but also reduces the security risk of the data stored in the storage services by checking the data integrity with the help of IMS. We define data integrity protocols for a number of different scenarios, and demonstrate the feasibility of the proposed architecture, service and protocols by implementing them on a public cloud, Amazon S3. We also study the impact of our proposed protocols on the performance of the storage service and show that the benefits of our approach outweigh the little penalty on the storage service performance.