Bio: Mohanasundaram R is an academic researcher from VIT University. The author has contributed to research in topics: Cryptography & Encryption. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
TL;DR: In today's world, most of the people are using social networks for day-to-day activities and the most frequently used social sites are Facebook, Twitter, Google+, etc.
Abstract: In today's world, most of the people are using social networks for day-to-day activities. The most frequently used social sites are Facebook, Twitter, Google+, etc. These popular social networks ar...
TL;DR: The proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment.
Abstract: Purpose There are various system techniques or models which are used for access control by performing cryptographic operations and characterizing to provide an efficient cloud and in Internet of Things (IoT) access control. Particularly in cloud computing environment, there is a large-scale distribution of these traditional symmetric cryptographic techniques. These symmetric cryptographic techniques use the same key for encryption and decryption processes. However, during the execution of these phases, they are under the problems of key distribution and management. The purpose of this study is to provide efficient key management and key distribution in cloud computing environment. Design/methodology/approach This paper uses the Cipher text-Policy Attribute-Based Encryption (CP-ABE) technique with proper access control policy which is used to provide the data owner’s control and share the data through encryption process in Cloud and IoT environment. The data are shared with the the help of cloud storage, even in presence of authorized users. The main method used in this research is Enhanced CP-ABE Serialization (E-CP-ABES) approach. Findings The results are measured by means of encryption, completion and decryption time that showed better results when compared with the existing CP-ABE technique. The comparative analysis has showed that the proposed E-CP-ABES has obtained better results of 2373 ms for completion time for 256 key lengths, whereas the existing CP-ABE has obtained 3129 ms of completion time. In addition to this, the existing Advanced Encryption Standard (AES) scheme showed 3449 ms of completion time. Originality/value The proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment. The novelty of the research is that the proposed E-CP-ABES incorporates extensible, partially hidden constraint policy by using a process known as serialization procedure and it serializes to a byte stream. Redundant residue number system is considered to remove errors that occur during the processing of bits or data obtained from the serialization. The data stream is recovered using the Deserialization process.
TL;DR: The proposed method elaborates the much enhanced and actual key management system along with improved speed and less storage of the key along with increased security over Curve 25519, and which has a 255-bit prime number.
Abstract: Key management is very essential part in the cryptographic field where the key must be maintained very secure so that the problem of maintaining of the key is important. The proposed method elaborates the much enhanced and actual key management system along with improved speed and less storage of the key. The policy of managing the key is mandatory for producing a key and required to take care to the storage as well as processing of it in the cloud scenario where security of the data is up most important thing when number cloud users are more in the network to use several applications. Making use of several algorithms by considering Diffie Hellman by combing with the Elliptic Curve will provide most effective in managing of the keys with much lesser key size. In this paper key exchange and shared key generation with Curve 448 is considered the practical results shows that effective key generation and exchange of the shared key. It obtains a 224-bit security level which delivers increased security over Curve 25519, and which has a 255-bit prime number. The proposed elliptic curves which are quicker and simpler to implement than other prime-order curves which are applicable for most of the applications. Hamburg chose the Solinas trinomial prime base p=2 448 -2 224 -1 which provides faster Karatsuba multiplication.
TL;DR: The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality chara in users.
Abstract: The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality chara
TL;DR: In this paper , a novel random graph generator using a mixture of Gaussian distributions is introduced, and the community sizes of the generated network depend on the given Gaussian distribution. But, the performance of these algorithms can be varied based on the network structure.
Abstract: Identification of sub-networks within a network is essential to understand the functionality of a network. This process is called as 'Community detection'. There are various existing community detection algorithms, and the performance of these algorithms can be varied based on the network structure. In this paper, we introduce a novel random graph generator using a mixture of Gaussian distributions. The community sizes of the generated network depend on the given Gaussian distributions. We then develop simulation studies to understand the impact of density and sparsity of the network on community detection. We use Infomap, Label propagation, Spinglass, and Louvain algorithms to detect communities. The similarity between true communities and detected communities is evaluated using Adjusted Rand Index, Adjusted Mutual Information, and Normalized Mutual Information similarity scores. We also develop a method to generate heatmaps to compare those similarity score values. The results indicate that the Louvain algorithm has the highest capacity to detect perfect communities while Label Propagation has the lowest capacity.
TL;DR: The Co-Membership-based Generic Anomalous Communities Detection Algorithm (CMMAC) as discussed by the authors is a generic method that utilizes the information of vertices co-membership in multiple communities.
Abstract: Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities’ sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community’s vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.