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Internet of things (IoT) is a new technology uses sensors, networking, big data, and artificial intelligence technology to deliver complete systems for a product or service. Billions of devices are expected to be associated into the system and that shall require huge distribution of networks as well as the process of transforming raw data into meaningful inferences. Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of RFID, wireless, mobile and sensor devices. A wide range of Internet of Things applications have been developed and deployed in recent. In an effort to understand the development of IoT in industries, this paper reviews the current research of IoT, key enabling technologies, major IoT applications in industries and identifies research trends and challenges.

RFID, IoT, PDA’s, Smartphones, WSN, SOA, Sensors, Objects

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Bagadhi Sateesh, "Introduction to Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1086-1090, 2018.

Text clustering is an unsupervised data mining technique which involves the process of classifying an unlabeled dataset into the groups of similar data objects. These groups are known as clusters; each cluster consists of data objects such that the data objects are more similar within the same group and dissimilar to the data objects of other groups. There is a variety of text clustering techniques used to compute the similarity among the given unlabeled dataset patterns. Moreover, huge literature is available on clustering algorithms and a comprehensive survey would also be an immense task. The purpose of this paper is an attempt to explore the text clustering techniques and to facilitate the researchers for the future inventions. In this paper, literature survey of different text clustering techniques has been performed and presented an analysis of various studies in this area. After reviewing various text clustering techniques from different aspects, this paper suggests research directions for the researchers in this field that can be proved useful for the researchers. Survey of text clustering techniques is performed for the English text/documents as well as for the documents in vernaculars like Gurumukhi script.

Text clustering, Clustering techniques, Data mining techniques, Unsupervised learning, Machine learning

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Mukesh Kumar, Amandeep Verma, "Text Clustering Techniques : A Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1091-1099, 2018.

Insurance of computer assets and put away archives is a vital issue in this day and age. Intruders have made numerous triumphant endeavors to topple esteemed organization systems. In spite of the fact that the present security arrangements, for example, firewalls and hostile to infection programming have their critical parts in securing associations however they don`t identify a wide range of attacks of the present digital world. Intrusion detection is a system used to identify different attacks on a system. There are numerous Intrusion detection Systems (IDSs) accessible today. This paper gives a brief introduction about intrusion detection system and its components. Further, classification of intrusion detection system is discussed. Also various researches done in previous years are discussed.

intrusion detection system, data mining, confidentiality, integrity, availability, intrusion detector.

[1] Sanjay Sharma, R. K. Gupta, “Intrusion Detection System: A Review”, International Journal of Security and Its Applications, Vol. 9, No. 5, pp. 69-76, 2015.
[2] Sheenam, Sanjeev Dhiman, “Comprehensive Review: Intrusion Detection System and Techniques”, IOSR Journal of Computer Engineering, Vol. 18, Issue. 4, pp. 20-25, 2016.
[3] Rajni Tewatia, Asha Mishra, “Introduction To Intrusion Detection System: Review”, International Journal of Scientific & Technology Research, Vol. 4, Issue. 5, pp. 219-223, 2015.
[4] D. Ashok Kumar, S. R. Venugopalan, “Intrusion Detection Systems: A Review”, International Journal of Advanced Research in Computer Science, Vol. 8, No. 8, pp. 356-370, 2017.
[5] Kajal Rai, M. Shyamala Devi, “Intrusion Detection Systems: A Review”, Journal of Network and Information Security, Vol. 1, Issue. 2, pp. 15-21, 2013.
[6] Shadi Aljawarneh, Muneer Bani Yassein, Mohammed Aljundi, “An enhanced J48 classification algorithm for the anomaly intrusion detection systems”, Springer, 2017.
[7] Varsha Singh, Shubha Puthran, Avanish Tiwari, “Intrusion Detection Using Data Mining with Correlation”, IEEE, International Conference for Convergence in Technology, pp. 620-625, 2017.
[8] Sandeep Kaur, Dr. Sheetal Kalra, “Disease Prediction using Hybrid K-means and Support Vector Machine”, IEEE, 2016.
[9] Roshan Chitrakar, Huang Chuanhe, “Anomaly based Intrusion Detection using Hybrid Learning Approach of combining k-Medoids Clustering and Naïve Bayes Classification”, IEEE, 2012.
[10] Manish Kumar, Dr. M. Hanumanthappa, Dr. T. V. Suresh Kumar, “Intrusion Detection System Using Decision Tree Algorithm”, IEEE, pp. 629-634, 2012.
[11] Imtiaz Ullah, Qusay H. Mahmoud, “A Filter-based Feature Selection Model for Anomaly-based Intrusion Detection Systems”, IEEE, International Conference on Big Data, pp. 2151-2159, 2017.
[12] Luigi Coppolino, Salvatore D’Antonio, Alessia Garofalo, Luigi Romano, “Applying Data Mining Techniques to Intrusion Detection in Wireless Sensor Networks”, IEEE, International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 247-254, 2013.
[13] Saad Mohamed Ali Mohamed Gadal, Rania A. Mokhtar, “Anomaly Detection Approach using Hybrid Algorithm of Data Mining Technique”, IEEE, International Conference on Communication, Control, Computing and Electronics Engineering, 2017.
[14] Jithin Mathew, S. Ajikumar, "Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Vol. 2, Issue. 2, pp.92-97, March-April.2017.
[15] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, Int. J. Sci. Res. in Network Security and Communication, Vol. 5, Issue. 6, pp. 5-8, Dec 2017.
Authors Profile

S. Soni, P. Sharma, "Review of Hybrid Intrusion Detection System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1100-1104, 2018.

Mobile devices that run Android operating system are widely used. The applications running in Android mobiles can have malicious permissions due to malware. In other words, Android applications might spread malware which can sabotage valuable data. Therefore it is essential to have mechanism to classify malware and benign mobile applications running in Android phones. Since Android mobile applications run in the confines of mobile devices and associated servers, it is very challenging task to detect Android malware. Many solutions came into existence to detect malware applications. Of late Abawajy et al. proposed a technique known as Iterative Classifier Fusion System (ICFS) which employs classifiers iteratively with fusion to generate a final classifier for effective detection of malware. They combined NB tree classifier, Multilayer perception and Lib SVM with polynomial kernel to achieve this. However, the system does not focus on reduction or pruning of Android application permissions so as to build a classifier that reduces time and space complexity. In the proposed system, a methodology is proposed that focuses on reduction or pruning of android application permissions and ranking them in order to build a classifier that reduces time and space complexity. The classifier modelled with best ranked permissions can be representative of all permissions as least significant permissions are pruned to reduce search space. This paper built a prototype application to demonstrate proof of the concept. The experimental results revealed that the proposed system performs better in improving detection accuracy besides precision and recall measures.

Malware, malware detection technique, pruning, ranking

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[3] J. Walls and K.-K. R. Choo, “A review of free cloud-based antimalware apps for Android,” in Proceedings of 2015 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Trust Com 2015, vol. 1, pp. 1053–1058, 2015.
[4] S. Naval, V. Laxmi, M. Rajarajan, M. S. Gaur, and M. Conti, “Employing program semantics for malware detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 12, pp. 2591–2604, 2015.
[5] P. Faruki, S. Bhandari, V. Laxmi, M. Gaur, and M. Conti, “Droid analyst : Synergic app framework for static and dynamic app analysis,” Studies in Computational Intelligence, vol. 621, pp. 519–552, 2015.
[6] L. Sinha, S. Bhandari, P. Faruki, M. S. Gaur, V. Laxmi, and M. Conti, “Flow Mine : Android app analysis via data flow,” in Proceeding of the 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016, pp. 435–441, 2016.
[7] J. Abawajy, M. Chowdhury, and A. Kelarev, ”Hybrid Consensus Pruning of Ensemble Classifiers for Big Data Malware Detection,” IEEE Transaction on Cloud Com put 3(2):111, 2017.
[8] S. Sheen, R. Anitha, and V. Natarajan, “Android based malware detection using a multi feature collaborative decision fusion approach,” Neuro computing, vol. 151, pp. 905–912, 2015.
[9] S. Naval, V. Laxmi, M. S. Gaur, S. Raja, M. Rajarajan, and M. Conti, “Environment-reactive malware behaviour: Detection and categorization,” in Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance, ser. LNCS, vol. 8872, pp. 167–182, 2015.
[10] F. Daryabar, A. Dehghantanha, F. Norouzi, and F. Mahmoodi, “Analysis of virtual honey net and vlan-based virtual networks,” in International Symposium on Humanities, Science and Engineering Research, SHUSER 2011, pp. 73–77, 2011.
[11] K. Zhao, D. Zhang, X. Su, and W. Li, “Fest: A feature extraction and selection tool for Android malware detection,” in 20th IEEE Symposium on Computers and Communication, ISCC 2015, pp. 714–720, 2015
[12] J. Abawajy, A. Kelarev “Iterative Classifier Fusion System for the Detection of Android Malware”. IEEE Transactions on Big Data, Vol. 5, No. 4, p1-12, 2017.

Ramisetti Uma Maheswari, R Raja Sekhar, "Pruning and Ranking Based Classifier for Efficient Detection of Android Malware," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1105-1109, 2018.

Now these days educational institutes are facing the challenge of managing the full technologies stack for the students. Most of the institutes are managing their infrastructure on windows operating system. It will take lots of investments to manage the infrastructure on Microsoft technologies like windows operating system, windows server, SQL server, visual studio etc. Our main aim to minimize this investment for the educational institutes. So we want to develop an open source operating system based on linux with simplified UI and experience i.e. Zinc OS. Institutes can use Zinc OS and they can install the windows applications on our operating system. We studied about the development of a common kernel that can support Linux and Windows system calls and provide a common platform for Linux packages and windows software’s. Linux Kernel supports ELF based executable formats but Windows NT Kernel supports PE based executable formats. So it is very difficult to develop a common kernel and operating system which can support to Linux and Windows applications. We provide support for ELF and PE based executable format by using windows application compatibility layer (wine library) on main operating system that converts the windows application calls into linux system calls.

Zinc OS, Wine Library, Linux Kernel, Ubuntu OS

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[5] K. S. Rani, “Open Source Software: A Prominent Requirement of Information Technology” International Journal of Scientific Research in Network Security and Communication (ISSN: 2321-3256), ol.6 , Issue.2, pp.24-29, Apr-2018
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U. Kushwaha, "Zinc OS-Linux Based OS for Educational Institutes," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1110-1115, 2018.

Sentiment analysis is a research topic in the field of text mining. In today’s world it plays an important role as we are living in the age of digital world where each and every work is based on internet. These websites are working totally based on online review of various users. Sentiment analysis has gained focus in recent world due to increase in opinion rich web sources such as twitter, online review of products. This paper presents a review of different machine learning algorithms used for Sentiment analysis. A comparative study is being made on decision tree, Naive Bayes Algorithm and Neural Network. Our system is being tested on four products with positive, negative and neutral review. The system processes the text collected as dataset for review and accordingly it is being trained to classify these reviews efficiently.

Sentiment analysis, text mining, machine learning, NLP, Decision Tree, Naive Bayes, Neural Network algorithms

[1] Emma Haddi, Xiaohui Liu, Yong Shi, “The Role of Text Pre-processing in Sentiment Analysis”, Information Technology and Quantitative Management (ITQM2013), Procedia Computer Science 17 ( 2013 ) 26 – 32
[2] A. Suresh , C.R. Bharathi, “Sentiment Classifi cation using Decision Tree Based Feature Selection”, IJCTA, 9(36), 2016, pp. 419-425
[3] Anuja P Jain, Padma Dandannavar, “Application of Machine Learning Techniques to Sentiment Analysis”
[4] Lopamudra Dey, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, Sweta Tiwari, ” Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier”
[5] Kim Schouten and Flavius Frasincar, ” Survey on Aspect-Level Sentiment Analysis”, IEEE TRANSACTIONS on knowledge and data engineering, vol. 28, no. 3, march 2016.
[6] Alvaro Ortigosa , José M. Martín, Rosa M. Carro, “Sentiment analysis in Facebook and its application to e-learning”, Computers in Human Behavior 31 (2014) 527–541

[7] Raymond Y.K. Lau , Chunping Li, Stephen S.Y. Liao , “Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis”, Decision Support Systems 65 (2014) 80–94.
[8] Zhi-Hong Deng , Kun-Hu Luo, Hong-Liang Yu, “A study of supervised term weighting scheme for sentiment analysis”, Expert Systems with Applications 41 (2014) 3506–3513
[9] Tahura Shaikh, Dr. Deepa Deshpande, “Feature Selection Methods in Sentiment Analysis and Sentiment Classification of Amazon Product Reviews”, International Journal of Computer Trends and Technology (IJCTT) – Volume 36 Number 4 - June 2016
[10] Min-Ling Zhang Zhi-Hua Zhou, “A Review on Multi-Label Learning Algorithms”, IEEE TRANSACTIONS ON Knowledge And Data Engineering, VOL. 26, NO. 8, AUGUST 2014
[11] Bo Pang and Lillian Lee, Shivakumar Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques”
[12] Arman S. Zharmagambetov, Alexandr A. Pak, “Sentiment Analysis of a document using deeplearning approach and decision trees”
[13] Ahmed Alsaffar, Nazlia Omar, “Study on Feature Selection and Machine Learning Algorithms For Malay Sentiment Classification”, 2014 International Conference on Information Technology and Multimedia (ICIMU), November 18 – 20, 2014, Putrajaya, Malaysia
[14] V.K. Singh, R. Piryani, A. Uddin, P. Waila, Marisha,” Sentiment Analysis of Textual Reviews”, 2013 5th International Conference on Knowledge and Smart Technology (KST)
[15] V. K. Singh, M. Mukherjee & G. K. Mehta, “Combining Collaborative Filtering and Sentiment Analysis for Improved Movie Recommendations”, In C. Sombattheera et. al. (Eds.):Multi-disciplinary Trends in Artificial Intelligence, LNAI 7080, Springer-Verlag, Berlin-Heidelberg, pp. 38-50, 2011.
[16] V. K. Singh, M. Mukherjee & G. K. Mehta, “Combining a Content Filtering Heuristic and Sentiment Analysis for Movie Recommendations”, In K.R. Venugopal & L.M. Patnaik (Eds.): ICIP 2011, Aug. 2011, CCIS 157, pp. 659-664, Springer, Heidelberg, 2011.
[17] P. Turney, “Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews”, Proceedings of ACL-02, 40th Annual Meeting of the Association for Computational Linguistics, pp. 417-424, Philadelphia, US, 2002.
[18] Walaa Medhat ,Ahmed Hassan, “Sentiment analysis algorithms and applications:A survey” Shams Engineering Journal (2014) 5, 1093–1113
[19] S. Milstein, A. Chowdhury, G. Hochmuth, B. Lorica, and R. Magoulas. “Twitter and the micromessaging revolution:Communication, connections” An OReilly Radar Report. 54 pages, November 2008.
[20] J. Ashok Kumar and S. Abirami, "An Experimental Study Of Feature Extraction Techniques In Opinion Mining," International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), Vol.4, No.1,February 2015.
[21] GurneetKaur and AbhinashSingla, “Sentimental Analysis of Flipkart reviews using Naïve Bayes and Decision Tree algorithm,” International Journal Of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 5, ISSN: 2278- 1323, January 2016.
[22] Aashutosh Bhatt, Ankit Patel, Harsh Chheda and Kiran Gawande, "Amazon Review Classification and Sentiment Analysis", International Journal of Computer Science and Information Technologies, Vol. 6, ISSN:0975-9646, 2015.
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S. Manna, "Sentiment Analysis based on Different Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1116-1120, 2018.

Stream ciphers are widely used in wireless communications to transforms the data and delivers through wireless channel. This paper presents various stream ciphers used for data encryption in different wireless communication technologies. The main purpose of this paper is to provide information on various stream ciphers used in wireless communications.

Stream Ciphers, Wireless Communications, GSM, Bluetooth, WEP

[1] Goutam Paul and Subhamoy Maitra (2012), RC4 Stream Ciphers and its variants, CRC Press ,Taylor & Francis Group.
[2] RC4 Link
https://www.vocal.com/cryptography/rc4-encryption-algoritm/- R
[3] Lazar Stošić, Milena Bogdanović (2012), RC4 stream cipher and possible attacks on WEP, International Journal of Advanced Computer Science and Applications, Vol. 3, No. 3, 2012.
[4] Shenam Chugh, Kamal(2015),Securing data transmission over Wireless LAN (802.11) by redesigning RC4 Algorithm, 2015,IEEE International Conference on Green Computing and Internet of Things (IeGCloT).
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[9] Marc Briceno, Ian Goldberg, DavidWagner (1998): A pedagogical implementation of A5/1,http://www.gsm-security.net/papers/a51.shtml.
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[12] Eli Biham, Orr Dunkelman (2000), Cryptanalysis of the A5/1 GSM Stream Cipher,Progress in Cryptology - INDOCRYPT 2000, First International Conference in Cryptology in India, Calcutta, India, (pp.43-51).
[13] Ross Anderson (1998): The GSM cipher, http://groups.google.com/group/sci.crypt/msg/ba76615fef32ba32.
[14] Ch. Naveen, Vishal R. Satpute (2016), Image encryption technique using improved A5/1 cipher on image bit planes for wireless data security, International Conference on Microelectronics, Computing and Communications (MicroCom), 2016.
[15] Komal Rege, Nikita Goenka, Pooja Bhutada, Sunil Mane(2013) ,Bluetooth Communication using Hybrid Encryption Algorithm based on AES and RSA, International Journal of Computer Applications (0975 – 8887) Volume 71– No.22, June 2013.
[16] Wuling Ren and Zhiqian Miao(2010), "A Hybrid Encryption Algorithm Based on DES and RSA in Bluetooth Communication", Second International Conference on Modelling, Simulation and Visualization Methods, 2010.
[17] Bluetooth link-
https://krazycoder.wordpress.com/2010/09/08/basics-of-bluetooth-security/
[18] 3GPP specifications: Link
http://www.3gpp.org/specifications
[19] ETSI/SAGE Specification (2006): Specification of the 3GPP Confidentiality and Integrity Algorithms UEA2 & UIA2, Document 2: SNOW 3G Specification, September 2006.
[20] ETSI/SAGE Technical report (2006): Specification of the 3GPP Confidentiality and Integrity Algorithms UEA2 & UIA2. Document 5: Design and Evaluation Report, Version 1.1, September 2006.
[21] ETSI/SAGE Specification(2011): Specification of the 3GPP Confidentiality and Integrity Algorithms 128-EEA3 & 128-EIA3. Document 1: 128-EEA3 and 128-EIA3 Specification; Version: 1.6; Date: 1st July 2011.
[22] Hadia M.S. El Hennawy , Alaa E.A. Omar , Salah M.A. Kholaif(2015), LEA: Link Encryption Algorithm Proposed Stream Cipher Algorithm, Ain Shams Engineering Journal (2015) 6, 57–65.
[23] Mahdi Madani, Ilyas Benkhaddra, Camel Tanougast, Salim Chitroub, and Loic Sieler (2017), Digital Implementation of an Improved LTE Stream Cipher Snow-3G Based on Hyperchaotic PRNG, Hindawi Security and Communication Networks Volume 2017, article ID 5746976, 15 pages.
[24] GSM link- http://pages.cpsc.ucalgary.ca/~szrrizvi/cpsc329/t2.html
[25] Romil Gandhi, Amitha Nair, Jason D’Souza (2014), GSM Networks: Substantiation of GSM Stationed algorithm, International Journal of Scientific & Engineering Research, Volume 5, Issue 7, July-2014 153.
[26] Khan,Shahzad, Shahid Peracha, Zain Ul Abideen Tariq(2014), Evaluation of Cryptanalytic Algorithm for A5/2 Stream Cipher, International Journal of Computer Science and Information Security, Vol. 12, No. 4, April 2014.
[27] Voice and packet encryption -https://security.stackexchange.com/questions/99559/cellular-encryption-algorithms-currently-in-use-globally
[28] Harsh Bhasin, Neha Kathuria Cryptography Automata Based Key Generation, International Journal of Scientific Research in Network Security and Communication , p 15-17,Volume-1, Issue-2, June- 2013
[29] P. Sharma, D.Mishra,V.K. Sarthi,P. Bhatpahri,R. Shrivastava,Visual Encryption Using Bit Shift Technique, International Journal of Scientific Research in Computer Sciences and Engineering, vol.5, Issue.3, pp.57-61, June (2017)

Y.Nagendar, V. Kamakshi Prasad, Allam Appa Rao, G.Padmavathi, "Applications of Stream ciphers in wireless communications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1121-1126, 2018.

Several Symmetric Key based algorithms exist for securing data of big size. In Order to investigate the efficient one , performance analysis is often desirable activity over variety of parameters including number of cores or processors. In present work one symmetric key algorithm has been considered for analysis for n=1( Serially) and (n =2) for Parallel analysis, (where n is number of core). The relative gain of serial implementation and tested for efficiency indicates that encryption of data chunks in parallel is the for blowfish algorithm. The findings in this study provides a useful direction to users and designers of low power handheld devices and IOT to exploit faster encryption and decryption of data with more number of cores.

Symmetric Key Cryptography, Blowfish , parallel implementation

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[20] Shivlal Mewada, Pradeep Sharma, S.S Gautam, “Exploration of Efficient Symmetric AES Algorithm”, IEEE 2016 Symposium on Colossal Data Analysis and Networking (CDAN), pp.1-5, Mar, 2016. DOI: dx.doi.org/10.1109/CDAN.2016.7570921
[21] Shivlal Mewada, Sharma Pradeep, Gautam S.S., “Exploration of Efficient Symmetric Algorithms”, IEEE 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)”, pp.663-666, March, 2016, DOI: http://www.dx.doi.org/10.13140/RG.2.2.22077.05607

Manju Suchdeo, Deepak Mawane, Mahek Negandhi, Shipra Sarkar, Shaligram Prajapat, "Towards Performance Analysis of Symmetric Key Algorithm on n-Core Systems: An IOT Perspective," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1127-1129, 2018.

In the age of computer driven decision making the Data Science has become a vital important area for the parties storing the data. For the efficient use of available data; users need to excel in better Data Mining through robust Machine Learning techniques. Data mining applications are intensively used in government and corporate sector to analyze data for prediction, pattern recognitions, and classification. Accuracy of data mining algorithm depends on volume of training data. Advancement in computer and communication technologies allowed distributed computing environment, where multiple clients/parties can conduct joint learning process by incorporating distributed data. Distributed data may be arbitrary partitioned among parties. Recent data mining application uses power of cloud computing to execute complex computation involved in learning process. Despite of these advancements, individual or organizations holding data are reluctant to share their sensitive data due to fear of privacy breach and losses. Privacy Preserving Data Mining (PPDM) is solution to protect personal information while sharing it in distributed environment. Privacy preservation is achieved by data randomization or encryption techniques. Robust security to personal information and more accuracy in mining applications, offer more popularity to privacy preservation encryption techniques than data randomization techniques. Homomorphic encryption is one of the popular encryption techniques, where users can perform operations on cipher text; the results are similar to operations on their respective plaintexts. In present survey paper some data mining techniques like- ANN, RDT, SVM and Deep Learning, based on distributed partitioned data such are reviewed in special context to privacy preservation.

Data mining, Privacy preservation, homomorphic encryption, ANN, RDT, SVM, Deep Learning

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Authors Profile
S. B. Javheri in pursed Bachelor of Engineering from North Maharashtra University, Jalgaon, Marashatra, India in 1998 and Master of Engineering from B. V. D. University, Pune, Maharashtra, India in year 2009. He is currently pursuing Ph.D. from S.G.G.S.I.E.& T., Nanded, Maharashtra, India and currently working as Associate Professor in Department of Computer Engineering , JSPM’s Rajarshi Shahu College of Engineering, Pune since 2004. He has published 12 research papers in reputed international journals/conferences. His main research work focuses on information security, machine learning, Neural network. He has 19 years of teaching experience.

U. V. Kulkarni has obtained Bachelor of Engin- eering degree in Electronics from Marathwada University, Aurangabad, Maharashtra, India in 1987. He completed Master of Engineering in system software from Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India in 1992. He has completed his Ph.D. in Electronics and Computer Science Engineering in 2002 from Swami Ramanand Teerth Marathwada University Nanded, Maharashtra, India. He is currently working as Professor and Head in Computer Science and Engineering Department at SGGSIE&T (Autonomous), Nanded, Maharashtra, India. He has received National Level Gold Medal and Computer Engineering Division Prize for the paper published in the Journal of Institution of Engineers, titled as Fuzzy Hypersphere Neural Network Classifier, May 2004 and the best paper award for the research paper presented in international conference held at Imperial College London, U.K., 2014. He has published many research papers in reputed National and International Journals. His areas of interest include Microprocessors, Data Structures, Distributed Systems, Fuzzy Neural Networks, and Pattern Classification. He has more than 30 years of teaching experience.

S. B. Javheri, U. V. Kulkarni, "A Survey on Privacy Preserving Machine Learning Techniques for Distributed Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1130-1139, 2018.

In this paper, a picture of energy awareness and parameters in Intelligence paradigm of ANN and fuzzy logic with respect to WSNs is explained so that ultimate intelligence like the human can be optimized by the hybrid technique which enables the technology to handle issues in a reliable fashion. An intelligent paradigm of ANN and fuzzy logic features shows its authenticity and adaptation of hybrid approach to achieve more optimized and deterministic result.

WSNs(Wireless Sensor Networks), ANN(Artificial Neural Network), and Fuzzylogic

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Supriya Singh, Awadhesh Kumar, "Intelligence Paradigm comparative study using ANN and fuzzy logic with respect to WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1140-1143, 2018.