R. Mangaiyarkarasi

Bio: R. Mangaiyarkarasi is an academic researcher from VIT University. The author has contributed to research in topics: Information privacy & Encryption. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Supporting reputation based trust management enhancing security layer for cloud service models

Abstract: In the existing system trust between cloud providers and consumers is inadequate to establish the service level agreement though the consumer's response is good cause to assess the overall reliability of cloud services. Investigators recognized the significance of trust can be managed and security can be provided based on feedback collected from participant. In this work a face recognition system that helps to identify the user effectively. So we use an image comparison algorithm where the user face is captured during registration time and get stored in database. With that original image we compare it with the sample image that is already stored in database. If both the image get matched then the users are identified effectively. When the confidential data are subcontracted to the cloud, data holders will become worried about the confidentiality of their data in the cloud. Encrypting the data before subcontracting has been regarded as the important resources of keeping user data privacy beside the cloud server. So in order to keep the data secure we use an AES algorithm. Symmetric-key algorithms practice a shared key concept, keeping data secret requires keeping this key secret. So only the user with private key can decrypt data.

Friend suggestion in social network based on user log

Abstract: Simple friend recommendation algorithms such as similarity, popularity and social aspects is the basic requirement to be explored to methodically form high-performance social friend recommendation. Suggestion of friends is followed. No tags of character were followed. In the proposed system, we use an algorithm for network correlation-based social friend recommendation (NC-based SFR).It includes user activities like where one lives and works. A new friend recommendation method, based on network correlation, by considering the effect of different social roles. To model the correlation between different networks, we develop a method that aligns these networks through important feature selection. We consider by preserving the network structure for a more better recommendations so that it significantly improves the accuracy for better friend-recommendation.

Mutual protection in a cloud computing environment

Abstract: The term “cloud computing” has emerged as a major ICT trend and has been acknowledged by respected industry survey organizations as a key technology and market development theme for the industry and ICT users in 2010. However, one of the major challenges that faces the cloud computing concept and its global acceptance is how to secure and protect the data and processes that are the property of the user. The security of the cloud computing environment is a new research area requiring further development by both the academic and industrial research communities. Today, there are many diverse and uncoordinated efforts underway to address security issues in cloud computing and, especially, the identity management issues. This paper introduces an architecture for a new approach to necessary “mutual protection” in the cloud computing environment, based upon a concept of mutual trust and the specification of definable profiles in vector matrix form. The architecture aims to achieve better, more generic and flexible authentication, authorization and control, based on a concept of mutuality, within that cloud computing environment.

FAFinder: Friend Suggestion System for Social Networking

Abstract: The emergence of social networking has led people to stay connected with friends, family, customers, colleagues or clients. Social networking can have social purposes, business purposes or both through sites such as Facebook, Instagram, LinkedIn, Twitter and many more. Recently, a large active social involvement have been seen from all echelons of society which keeps the friend circle increasing than never before. But, the friend suggestions based on one’s friend list or profile may not be appropriate in some situations. Considering this problem, in this paper, a Friend Suggestion System, FAFinder (Friend Affinity Finder) based on 5 major dimensions (attributes): Agreeableness, Conscientiousness, Extraversion, Emotional range and Openness is proposed. This will help in understanding more about the commonalities that one shares with the other on the basis of their behaviour, choices, likes and dislikes etc. The suggested list of friends are extracted from the People Database (containing details of the 5 dimensions of different people) by deploying the concept of Hellinger-Bhattacharyya Distance (H-B Distance) as a measure of dissimilarity between two people.

ED-BeCT : An enhanced dynamic behavioral cloud trust model to evaluate the trustworthiness of the cloud service provider

Abstract: Cloud computing is an evolving technology providing the delivery of on-demand calculating services to offer scalable resources dynamically in an economical manner. The companies that provide these types of services enable users to store their data in, and exchange information with a virtual space remotely. However, this fact makes this technology prone to various risks and security threats. Thus, in cloud environments, assessing the trustworthiness is a momentous challenge that requires much effort and time to build up users’ confidence to choose a trustworthy cloud service provider. Trust has been studied by many researchers using several techniques and evaluating various trust characteristics. In fact, conceptually, trust has a vague notion and in cloud computing, involves diverse trust characteristics such as security, reliability, auditability, multi-tenancy and so on. Although extensive research has been carried out on trust in cloud environments, no single study exists which evaluates trust with standard measures from different perspectives. In view of these issues, our objective in this thesis is to propose a comprehensive cloud trust model based on existing system and software quality standards as well as to consider the cloud services behavior to be compared with the proposed service level agreements (SLAs) to filter qualified cloud service providers. In the context of a proposal for an Enhanced Dynamic Behavioral Cloud Trust (ED-BeCT) model, we study the applicable system and software quality standards to find standard trust characteristics. We analyze the literature to derive the commonly recognized trust characteristics applicable for evaluating trust in cloud computing. Additionally, we derive proper measures for each trust characteristic to increase the accuracy of the proposed model in trust assessment. Concerning the different aspects of cloud computing, we generalize these trust measures for the three types of cloud service models (i.e. Software as a Service, Platform as a Service, Infrastructure as a Service). To consider the level of importance of each trust characteristic, we apply full consistency method (FUCOM) to calculate appropriate weights. In this method, the weights are calculated based on the mathematical transitivity conditions and equal relations between the weights and the comparative priorities of the trust characteristics. The final value of the trust is determined by applying the calculated weights in the normalized values of the trust measures in the SAW (Simple Additive Weighting) method. Our work thereby introduces a cloud trust model to overcome the deficiencies of the prior models proposed by other researchers. Throughout this thesis, the presented detail and analysis of the model shows that the proposed model is accurate and has the appropriate capabilities to calculate trust in cloud computing and evaluate the provided cloud services.