R. Jemina Priyadarsini ¹, L. Arockiam ¹

Affiliations
1 Department of Computer Science, St. Joseph’s College, Trichirapalli - 620002, Tamil Nadu, IN

Abstract

Objectives: Cloud environment requires scheduling of independent tasks with the available resources to minimize the total execution time and to optimize the resource utilization in cloud environment. Methods: Evolutionary algorithms are widely used to find the suboptimal solution of a problem. This work adopts a parallel approach that considers Bee Colony Optimization (BCO) in parallel with Particle Swarm Optimization (PSO) for cloud task scheduling. The proposed approach is named as Parallel Bee Colony Optimization Particle Swarm Optimization (PBCOPSO). Findings: The results show that the proposed approach minimizes Makespan with optimized resource utilization. It is observed that the proposed method improved resource utilization by an average of 5.0383% when compared with Min-Min algorithm and by an average of 3.7243% when compared with Improved Bee Colony Optimization (IBCO). Novelty of the Study: The proposed hybrid PBCOPSO enables improved search in the solution space due to the parallel execution of BCO and PSO leading to better final solution quality and lower execution time. Conclusion: Thus two metrics namely makespan and resource utilization are evaluated and an optimal task to resource mapping is achieved with hybridization.

Keywords

Bee Colony Optimization, Optimization, Particle Swarm Optimization, Resource Utilization, Scheduling

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R. Kalaichelvi ¹, L. Arockiam ²

Affiliations
1 Department of Computer Science, Karpagam University, Coimbatore - 641021, Tamil Nadu, IN
2 Department of Computer Science, St. Joseph’s College, Tiruchirappalli - 620002,Tamil Nadu, IN

Abstract

Objective: Cloud storage reduces the cost of IT infrastructure for businesses by sharing resources through internet. However, data stored on cloud has security issues and vulnerable to attacks by malicious hackers. The main objective of this paper is to propose a framework to address issues and attacks which compromise the confidentiality of outsourced data. Methods: The significant factor to ensure data security is confidentiality. Data partitioning, shuffling and encryption techniques are taken in this study to preserve confidentiality. Specifically blowfish and AES encryption techniques are modified to have better performance in terms of encryption time, decryption time, overall processing time, throughput and the degree of security. Findings: In the proposed framework, the data is split into multiple chunks and the chunks of data are shuffled. Then the shuffled chunks are encrypted using a hybrid technique which comprises of Enhanced Blowfish (EnBlo) and Enhanced Advanced Encryption Standard (EnAES) in order to provide data confidentiality. It further proposes an efficient retrieval scheme by appropriate decryption and a system to rearrange data chunks in order to get the original data at the client side. Conclusion: This research concludes, the EnBloAES provides extensive data security and adept at retrieval scheme by incorporating data confidentiality.

Keywords

Cloud Storage, Confidentiality, Data Outsourcing, Data Partition, Encryption, Shuffling

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S. Monikandan ¹, L. Arockiam ²

Affiliations
1 Department of MCA, Christhu Raj College, Panjappur, Tiruchirappalli – 620012, Tamil Nadu, IN
2 Departmentof Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli – 620102, Tamil Nadu, IN

Abstract

Security of data stored in the cloud is most important challenge in public cloud environment. Users are outsourced their data to cloud for efficient, reliable and flexible storage. Due the security issues, data are disclosed by Cloud Service Providers (CSP) and others users of cloud. To protect the data from unauthorized disclosure, this paper proposes a confidentiality technique as a Security Service Algorithm (SSA), named MONcryptto secure the data in cloud storage. This proposed confidentiality technique is based on data obfuscation technique. The MONcrypt SSA is availed from Security as a Service (SEaaS). Users could use this security service from SEaaS to secure their data at any time. Simulations were conducted in cloud environment (Amazon EC2) for analysis the security of proposed MONcrypt SSA. A security analysis tool is used for measure the security of proposed and existing obfuscation techniques. MONcrypt compares with existing obfuscation techniques like Base32, Base64 and Hexadecimal Encoding. The proposed technique provides better performance and good security when compared with existing obfuscation techniques. Unlike the existing technique, MONcrypt reduces the size of data being uploaded to the cloud storage.

Keywords

Cloud Service Providers, Cloud Storage, Confidentiality, Obfuscation, Security, Security Service Algorithm.

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A. Cecil Donald ¹, L. Arockiam ¹

Affiliations
1 Computer Science, St. Joseph’s College, Trichy - 620002, Tamil Nadu, IN

Abstract

Objectives: Authenticating the MobiCloud users using traditional passwords, biometrics is prone to shoulder surfing attack and need external device. Pattern Based User Authentication (PBUA) mechanism is introduced to identify and authenticate the users in MobiCloud environment. Methods/ Statistical Analysis: To authenticate the users, the whole process is divided into two phases namely, identification and authentication. Dynamic patterns are generated using random mathematical function to select the patterns from the Identity Management (IdM) Server. Pattern matching operation is done using the hash-based technique to match the image pattern sequences quickly with the actual pattern. Findings: Experimental results show that the proposed PBUA is strong enough against shoulder surfing attack. The time taken for pattern matching is very less when compared to existing pattern matching mechanisms. Application/Improvement: PBUA does not need any intermediate table as the patterns are matched directly with original patterns. The complexity of the algorithm is reduced by , thus the efficiency is improved n2 times. Pattern updation is not required which reduces the user’s burden.

Keywords

Authentication, Identity Management, Identification, Mobile Cloud, Pattern Matching.

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N. Veeraragavan ¹, L. Arockiam ¹

Affiliations
1 Department of Computer Science, St. Joseph’s College (Autonomous), Tiruchirappalli – 620002, Tamil Nadu, IN

Abstract

Objectives: In today's Technological World, Information Security is an essential aspect for the internet applications. Cloud computing is an increasing current class of services for any type of users of the internet. Authentication is a major security problem in the cloud system and internet. This paper proposes an Authentication Password Generation (APG) algorithm, Authentication Key Generation (AKG) and Authentication Verification Algorithm (Auth_V) for security in the cloud system. Methods/Statistical Analysis: Authentication is more important among other security parameters such as integrity, confidentiality and privacy. Authentication mechanism includes different cryptography techniques that can be used for securing the data in cloud systems. These proposed three algorithms are used in the authentication process of cloud environment. It executes the ASCII code of each value in the original data. Findings: Proposed APG, AKG and Auth_V authentication algorithms are implemented in .NET and deployed on the windows azures platform of cloud environment. These proposed authentication algorithms easily fit into any type of service in the cloud system. APG is used to create a password which generates the alphabets along the special characters. Application/Improvements: Security is more important in the cloud computing. The proposed APG, AKG and Auth_V algorithms have provided better authentication mechanism to the cloud user. These techniques are suitable for education, healthcare and agriculture based applications to securely access the data in a cloud computing environment.

Keywords

Authentication, Authentication Key Generation, Authentication Password Generation, Authentication Verification, Cloud Security, Cryptography.

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Amalraj Irudayasamy ¹, L. Arockiam ²

Affiliations
1 Periyar University, Salem - 636011, Tamil Nadu, IN
2 Computer Science, St. Joseph’s College, Trichy - 620002, Tamil Nadu, IN

Abstract

BackGround/Objectives: Anonymizing data sets through generalization satisfies certain privacy concerns such as k-anonymity that are broadly used as privacy conserving procedures. Parallel bottom-up generalization approach is introduced to anonymize huge datasets by map reduce structure on public cloud. A group of innovative map reduce jobs are formulated to perform the generalization in an exceedingly scalable manner. Methods/Statistical Analysis: Map Reduce, a widely-adopted parallel data processing framework is introduced, to address the privacy preservation problem with minimum information loss of the Bottom-Up Generalization (BUG) approach for large-scale data anonymization. To make full use of parallelism feature of Map reduce on cloud the whole process are split into two phases. Firstly, unique datasets are partitioned into a collection of lesser datasets, and these datasets are anonymized in parallel, giving intermediary outcome. Secondly, the intermediate results are combined and anonymized, to attain consistent k-anonymous data sets. Map Reduce concept is used to accomplish the computation in both phases. Findings: In this paper, investigational evaluation, results to gain high privacy preservation with minimum information loss in less execution time when compared to the existing approaches. The results demonstrate the insufficiency of the state-of-the art sub-tree anonymization approaches when handling large data sets. According to the tendencies of execution time and Information Loss, it is necessary and reasonable to choose MRBUG to perform parallel generalized data anonymization for large data according to the value of k. Applications/Improvements: Optimized, heuristic, and balanced scheduling approaches are expected to be developed towards overall scalable privacy preservation. It is believed that the structure of bottom-up generalization is amenable to several extensions that make it more practical. Incorporating different metrics and handling data suppressions in partial generalization is not necessarily require to have all child values generalized altogether. It is also possible to generalize numeric attributes without a pre-determined hierarchy and shall be taken up as a future work.

Keywords

Bottom-Up Generalization, Cloud, Data Anonymization, Map Reduce, Privacy Preservation

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