P. Tamil Selvan ¹, S. Veni ¹

Affiliations
1 Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore - 641021, Tamil Nadu, IN

Abstract

Background/Objectives: This paper proposes an Optimized Social Ant Based Sensitive Item Hiding (OSA-SIH) technique and expands the scope of quality privacy preservation for distributed data mining with optimal side effects on the original dataset. Methods/Statistical Analysis: in OSA-SIH technique, initially sensitive items for the given distributed dataset are evaluated using the social ant based relative item set distribution. Based on the evaluated dataset, optimal hiding of sensitive item is arrived with social ant based relative item set distribution even for larger item sets, ensuring time for optimal hiding. Next, sensitive item hiding is performed through multiplicative and transformational data perturbation. This data perturbation is based on socially cohesive relational rate between sensitive and non sensitive item sets, ensuring privacy preservation accuracy. The side effects on the modified dataset are checked for several users' requested item set distribution. Findings: The experimental results demonstrated that proposed technique out performed than the existing state of the art works in terms of privacy preservation accuracy, rate of side effects on the modified dataset, and time for optimal hiding. Improvement/Application: Experiments revealed that the proposed OSA-SIH technique

Keywords

Perturbation, Privacy Preserving Data Mining, Social Ant, Sensitive Item Hiding, Transformational Data Perturbation, Multiplicative Data Perturbation

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K. A. Rafidha Rehiman ¹, S. Veni ¹

Affiliations
1 Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore - 641021, Tamil Nadu, IN

Abstract

Background/Objectives: Internet of Things (IoT) has made significant changes in the real world and penetrates all aspects of human life. The user acceptance of IoT is enormously high and its widespread usage is because of the availability of smart phones and tablets. Wide adoption of IoT in the applications of each field always collecting sensitive information and provide a larger surface for intruders. So privacy preserved authentication and access controls are big challenges in its research area. Methods/Statistical Analysis: In this paper we introduced a novel algorithm based on Zero Knowledge Protocol and Accumulated Hashing to provide secure authentication to sensor enabled mobile devices in IoT. Also for ensuring confidentiality in communication proposed a new method for key exchange using current time. Findings: The proposed method fulfills the requirements of resource and battery constrained mobile devices in IoT when compared with traditional authentication and access control mechanisms for other applications.

Keywords

Authentication, Accumulated Hashing, Internet of Things, Mobile Security, Zero Knowledge Protocol

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S. Abhishek ¹, S. Veni ², K. A. Narayanankutty ¹

Affiliations
1 Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu, IN
2 Department of Electronics and Communication Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, Coimbatore – 641112, Tamil Nadu

Abstract

Background/Objectives: Compressed Sensing (CS) is an efficient sensing paradigm which guarantees reasonable reconstruction with less number of samples. We aim to increase the reconstruction quality of signals in CS. Methods/ Statistical Analysis: The behavior of random matrices is analyzed and an efficient method for improving the reconstruction quality is developed in CS based ECG reconstruction applications. The method is compared against Biorthogonal wavelet based approaches. Findings: Our analysis reveals that introduction of a modified column vector in the reconstruction matrix, which contains the sum of all columns of random matrix increases the reconstruction quality in CS applications. This idea was applied to different sparsifying domains and the results are very encouraging. We studied the effect of doing this on the singular values and both unitary matrices U and V. The first singular value (Σ) shot up making the condition number high, however there was not much change in the other singular values. The matrix U seems to remain random unitary matrix, where as matrix V has one value becoming unity in its rank space. Application/Improvements: Compared to wavelet based approaches the method shows reasonable improvement in Percentage Root Square Deviation (PRD).

Keywords

Compressed Sensing, ECG, PRD, Singular Values, Splines

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A. P. Nirmala ¹, S. Veni ²

Affiliations
1 New Horizon College of Engineering, Bangalore 560103, Karnataka, IN
2 Department of Computer Science, Karpagam University, Coimbatore − 641021, Tamil Nadu, IN

Abstract

Virtualization plays a vital role in cloud computing. It provides better manageability, availability, optimistic provisioning, scalability and resource utilization in current cloud computing environments. However the performance issue is a major concern in virtualization. The performance of the application running inside the virtual machine gets affected by the interference of the co-virtual machines. This approach provides a novel task scheduling mechanism that allocates the suitable resources to virtual machines which are running in parallel. An interference prediction scheme is proposed to utilize characteristics that are collected when an application running on virtual machines to maintain less system overhead. Nelder-mead method is employed in prediction to create relationship model from the observed response and control variables. A hybrid algorithm: Ant Colony Optimization and Cuckoo search algorithm with K-means++ is adopted for task scheduling process. This approach shows effective improvements in terms of throughput and execution time.

Keywords

Ant Colony Optimization, Cuckoo Search, K-means++ Algorithm, Performance Interference, Throughput, Virtualization.

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