The current Aadhar based centralized database, face an extensive denunciation because of their intrinsic limitations in securing privacy. Central Identities Data Repository (CIDR), an Indian government agency is responsible for storing and managing the entire country's data related to Aadhar project. Additionally, Unique Identification Authority of India (UIDAI) verifies the authenticity of every Indian citizen. In recent surveys, it is observed that CIDR is facing lot of privacy issues bacause of it's centralized database and plug leakages. Owing to the increasing prevelance of Aadhar and the Indian government's ram towards cashless transactions and digital economy, it is essential to work on data migration, analysis and performance using Big Data Analytics and Data Science.

Keywords: Aadhar, Big Data Analytics, Central Identities Data Repository, plug leakages, Unique Identification Authority of India.

[1] B. Dufrasne, A. Warmuth, J. Appel, et al. Introducing disk data migration. DS8870 Data Migration Techniques. IBM Redbooks. pp. 1–16. ISBN 9780738440606. (2017).
[2] B. Goes, Paulo, Design science research in top information systems journals. MIS Quarterly: Management Information Systems. 38 (1) (2014).
[3] Boyd, dana; Crawford, Kate, Six Provocations for Big Data. Social Science Research Network: A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. doi:10.2139/ssrn.1926431. (21 September 2011).
[4] C. Seiwert, P. Klee, L. Marinez, L, et al. Migration techniques and processes. Data Migration to IBM Disk Storage Systems. IBM Redbooks. pp. 7–30. ISBN 9780738436289. (2012).
[5] Hellerstein, Joe, Parallel Programming in the Age of Big Data. Gigaom Blog. (9 November 2008).

The digitalization of business processes and practices is inevitable. One trend, of interest to both business and hackers, is the increasing number of mobile versions of enterprise applications for employees and customers. Mostly, Apps developers particularly focused on application functionalities, not security. This is the main reason helping hackers hack apps, repackage apps, malicious code and reloading apps. It gives very harsh issues to customer's sensitive data. Applications not using encryption can be affected by such problems. The best thing is that mobile apps developer has to use the encryption framework to protect the user's data which makes at most impossible to crack the data. Bad data storage, outdated of antivirus and lack of
encryption principles are the causes vulnerabilities on mobile applications. This paper outlines best practices to avoid mobile app vulnerabilities and secure its information.

Keywords: Mobile applications, Anti-malware, Encryption, Vulnerabilities, Sensitive data, Security.

[1] https://www.androidauthority.com/google-play-store-vs-the-apple-app-store-601836/ ( refer for the second chapter )
[2] Available at https://play.google.com/store/apps/category/shopping
[3] https://economictimes.indiatimes.com/small-biz/security-tech/technology/why-mobile-apps-require-access-to-your-dataand-devicetools/ articleshow/52138161.cms
[4] Available at https://play.google.com/store/apps/category/entertainment
[5] https://play.google.com/store/apps/category/communication.

Nowadays, communication depends on radio waves for sharing information at distant place where wireless technology is popularly used for short communication and most of the applications are executed by mobile ph ones and people are facing problems in operating mobile apps by opening many apps knowingly or unknowingly and hence their data are stolen by hackers. Though applications have features to protect data by encryption mechanisms, it seems insufficient to protect vital information. Perpetrator hacks personal information of others which is the main task of cyber space attacks since the information relates to money and Business confidential data. It is seen that the personal information is structured data which use insufficient encryption technique in applications like Aadhar and Banking systems...........

Keywords: Encryption, Decryption, Security, 2D image, Applications.

[1]. Suresh, Anjali, and AS Remya Ajai. "VLSI implementation of text to image encryption algorithm based on private key encryption." Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on. IEEE, 2016.
[2]. Saraf, Kundankumar Rameshwar, Vishal Prakash Jagtap, and Amit Kumar Mishra. "Text and image encryption decryption using advanced encryption standard." International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) 3.3 (2014): 118-126.
[3]. Singh, Virendra Pal, et al. "A New Symmetric Key Encryption Algorithm Based on Jumbling Binary Sequence of Message." 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). IEEE, 2018.
[4]. https://info.townsendsecurity.com/bid/72450/what-are-the-differences-between-des-and-aes-encryption
[5]. https://searchsecurity.techtarget.com/definition/asymmetric-cryptography

Remote Sensing field is the fastest growing technology in digital world. Remote Sensing's contribution to technology development is very high and highly efficient. Each country has an important of the remote sensing because it provides more contribution for making from food production development till country security. Water management is the significant parameter to measure the development of a country. Water resource management output reflects the economic growth of a nation. Each country has been implementing water resource management with the help of satellites to overcome the drought problem. It has been benefiting from the digital technology of satellite artifacts for water needs, Water irrigation development, Marine water surveillance, Lake Connectivity, River water connection. Satellites perform specially for the particular field through its unique character. This paper helps to determine the water resource management satellites with the satellite technical specifications.

Keywords: Remote Sensing, Water Resource Management, Satellite Technology, Satellite artifacts, Lake connectivity, Marine water Surveillance.

[1] Behera MD (1999) Remote sensing and environment. Employment News (Government of India) XXIV(13):1–2
[2] Estud.av. "Water resources in the future: problems and solutions "Print version ISSN 0103-4014 On-line version ISSN 1806-9592 vol.22 no.63 São Paulo 2008 http://dx.doi.org/10.1590/S0103-40142008000200002
[3] Himanshu, S. K., G. Singh, and N. Kharola. "Monitoring of drought using satellite data." International Research Journal of Earth Sciences 3.1 (2015): 66-72.
[4] Yan, N., B. Wu, V. K. Boken, S. Chang, and L. Yang. 2014. "A Drought Monitoring Operational System for China Using Satellite Data: Design and Evaluation." Geomatics, Natural Hazards, and Risk. doi: 10.1080/19475705.2014.895964.
[5] Luis E. García, Diego J. Rodríguez, Marcus Wijnen, Inge Pakulski " Earth Observation for Water Resources Management Current Use and Future Opportunities for the Water Sector" ISBN:978-1-4648-0476-2, DOI:10.1596/978-1-4648-0475-5

Every year vacancies are tremendously increasing in both government and private sector. The number of fresh graduates is also increasing. Un-employment is also increasing every year. Latest research in India, percentage of un-employability increased. But there is scarcity in the recruitment of qualified and skilled graduates. Why un-employability after the immediate graduation? The main objective of this paper is to give the concepts about the reasons of unemployability by using soft computing techniques. Also construct the student model to make them qualitative for recruitment. It is essential to analyze the reason of increasing percentage of un-employability. The academic achievement of higher education is a deciding factor in the life of any student. Higher learning specializations that are offered by universities and higher technological educational institutes are best............

Keywords : ANN, EDM, Student Model.

[1]. "EducationalDataMining.org", 2013. Retrieved 2013-07-15.
[2]. R. Baker (2010) Data Mining for Education. In McGaw, B., Peterson, P., Baker, E. (Eds.) International Encyclopedia of Education (3rd edition), vol. 7, pp. 112-118. Oxford, UK: Elsevier.
[3]. G. Siemens, R.S.j.d. Baker (2012). "Learning analytics and educational data mining: towards communication and collaboration". Proceedings of the 2nd International Conference on Learning Analytics and Knowledge: 252–254.
[4]. C. Romero, S. Ventura. Educational Data Mining: A Review of the State-of-the-Art. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews. 40(6), 601-618, 2010.
[5]. "http://educationaldatamining.org/EDM2008/" Retrieved 2013-09-04

Data mining is the process of investigating large datas to identify patterns and established relationship to solve problems. This paper studies the survey of data mining with image processing algorithm and recent technology in plant disease, soil contamination and under water image with numerical and categorical data. The study involved with Unmanned Aerial Vehicle (UAV) which is an aircraft that flies without a human pilot onboard, controlled remotely or flown autonomously via pre-programmed flight plans or other automated guidance systems.

Keywords: Unmanned Aerial Vehicle (UAV), soil, plant disease, underwater image

[1]. Adriano Mancini, Jack Dyson, Emanuele Frontoni, Primo Zingaretti," Soil / Crop Segmentation from Remotely Sensed Data Acquired byUnmanned Aerial System", 2017 International Conference on Unmanned Aircraft Systems (ICUAS) June 13-16, 2017, Miami, FL, USA
[2]. Arya M S, Anjali K, Mrs.Divya Unni ," DETECTION OF UNHEALTHY PLANT LEAVES USING IMAGE PROCESSING AND GENETIC ALGORITHM WITH ARDUINO".
[3]. M. F. Yahya, M. R. Arshad," Image-Based Visual Servoing for Docking of an Autonomous Underwater Vehicle".
[4]. Savita N. Ghaiwat, Parul Arora "Detection and Classification of Plant Leaf Diseases Using Image processing Techniques: A Review", International Journal of Recent Advances in Engineering & Technology, ISSN (Online): 2347 - 2812, Volume-2, Issue - 3, 2014
[5]. I. Colomina and P. Molina, "Unmanned aerial systems for photogrammetry and remote sensing: A review," fISPRSg Journal of Photogrammetry and Remote Sensing, vol. 92, pp. 79 – 97, 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0924271614000501

Data mining is the process of discovering interesting knowledge patterns from large amount of data stored in database. It is an essential process where the intelligent techniques (i.e., machine learning, artificial intelligence, etc ) are used to extract the data patterns (i.e., features). The aim of data mining process is to extract the useful information from dataset and transform it into understandable structure for future use. Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed. In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.

Keywords - Data Mining, Machine learning, Feature Learning.

[1]. https://www.mathworks.com/discovery/machine-learning.html
[2]. https://www.import.io/post/data-mining-machine-learning-difference/
[3]. https://neilpatel.com/blog/data-mining/
[4]. https://importoioweb.staging.wpengine.com/solutions/machine-learning/
[5]. https://en.wikipedia.org/wiki/Machine_learning

Human brains are capable to produce novel solutions to the dynamically emerging security problems. But they are restricted with process automation, correctness, remembrance, knowledge representations, processing parallel task, increasing execution speed, mobile control and dealing with huge amount of data when compared to intelligent computational tools. In this proposed effort, various design aspects are framed that serves as a boundary to build an intelligent home security model with alarm signal to denote the unauthorized entry of an object. This model uses the advantage of artificial intelligent technique to attain high level home security system incorporated with traditional monitoring system.

Keywords- Artificial Intelligence, Object Recognition, Security, Authorization

[1]. Surinder Kaur, Rashmi Singh, Neha Khairwal & PType equation here.ratyk Jain 2016," Home Automation And Security System", Advanced Computational Intelligence: An International Journal (ACII), Vol.3, No.3, July 2016
[2]. Santoso Budijono, Jeffri Andrianto, Muhammad Axis Novradin Noor 2014," Design and implementation of modular home security system with short messaging system", EPJ Web of Conferences 68,00025DOI: 10.1051/epjconf/20146800025
[3]. Jayashri Bangali and Arvind Shaligra 2013," Design and Implementation of Security Systems for Smart Home based on GSM technology" International Journal of Smart Home Vol.7,No.6(2013),pp.201-208
[4]. Sadeque Reza Khan, Ahmed Al Mansur, Alvir Kabir, Shahid Jaman, Nahian Chowdhury 2012," Design and Implementation of Low Cost Home Security System using GSM Network" International Journal of Scientific & Engineering Research Volume 3, Issue 3, March -2012 1 ISSN 2229-5518
[5]. Ashraf Elfasakhany, Jorge Hernández, Juan Carlos García, Mario Reyes, Francisco Martell 2011," Design and Development of a House-Mobile Security System" Engineering, 2011, 3, 1213-1224 , doi:10.4236/ eng. 2011. 312151 Published Online December 2011 (http://www.SciRP.org/journal/eng)

Image Processing is a process of performing operations on an image with an intention of getting an improved image. It is also used to mining to get the hidden information in an image. Edge detection is the method of finding the boundaries of object in the image. Recently image processing techniques are being used in the prediction of diseases, where it helps to increase the classification accuracy. In this paper a novel edge detection method based on least square support vector machines is proposed to detect edges with the target of predicting the lung cancer. This paper uses sensitivity, specificity, accuracy and F1-measure as the metric tomeasure the performance. The results show that the proposed methods outperforms than other algorithm.

Keywords- Edge Detection, classification, SVM, image processing, accuracy.

[1]. A. Olaode, G. Naghdy and C. A. Todd, "Unsupervised Image Classification by Probabilistic Latent Semantic Analysis for the Annotation of Images," 2014 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Wollongong, NSW, 2014, pp. 1-8.
[2]. C. Kuo, H. H. Ho, C. H. Li, C. C. Hung and J. S. Taur, "A Kernel-Based Feature Selection Method for SVM With RBF Kernel for Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 1, pp. 317-326, Jan. 2014.
[3]. Diamant, E. Klang, M. Amitai, E. Konen, J. Goldberger and H. Greenspan, "Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 6, pp. 1380-1392, June 2017.
[4]. Guang-Xin LI, Ke Wang, Li-Bao Zhang, The fast algorithm of weighted multi-resolution image fusion, China J. Image Graph. 10 (12) (2005)1529–1536.

A Mobile ad-hoc network (MANET) is a wireless dynamic self-configurable network, which has no fixed infrastructure. Reduction of routing overhead is a main disquiet when a MANET routing protocol is residential.In AASR(Authenticated Anonymous Secure Routing), the request packets are authenticated by the group signature and to prevent the inferring of neighbouring nodes to the destination by the onion routing.In this paper,we propose the maliciousnode revocation scheme to secure the network communications, to detect the attackermisbehaviour node and to isolate it by the revocation process.The cluster based mechanism used in MANETS and compared it against the various performances through simulations.The results demonstrated positive performances against malicious nodes in the network.

Keywords- MANETS, Security, Cluster based revocation scheme, ABR, AASR.

[1]. A.Rajaram, Dr. S. Palaniswami, "Malicious Node Detection System for Mobile Ad hoc Networks", International Journal of Computer Science and Information Technologies, Vol. 1,NO. 2, 2010, 77-85.
[2]. H. Shen and L. Zhao, "ALERT: An Anonymous Locationbased Efficient Routing Protocol in MANETs," IEEE Transaction on Mobile Computing, Vol. 12, No. 6, pp. 1079– 1093, 2013.
[3]. J. Kong and X. Hong, "ANODR: ANonymous On-Demand Routing with Untraceable Routes for Mobile Ad hoc networks," in Proc. ACM MobiHoc'03, Jun. 2003, pp. 291–302.

Noise removal is one of the techniques in image processing. In this paper Fast Noise Suppression is used for gray scale images which are contaminated by Noise. Remote sensing images are affected by different types of Noise like Gaussian Noise, Speckle Noise and impulse Noise. In this paper Traditional Median filter and Fast Noise Suppression Algorithm is used for reducing the Noise rate, when compare to some other filters. In this the Proposed Fast Noise Suppression Algorithm gives better results when compared to Traditional Median filter.

Keywords- Remote Sensing Image, Traditional Median filter, Fast Noise Suppression Algorithm, Gaussian Noise.

[1]. Ranganath R., Navalgund, V. Jayaraman and P. S. Roy "Remote sensing applications: An overview" Special Section: Indian Space
[2]. Mr. Salem Saleh Al-amir, Dr. N.V. Kalyankar and Dr. S. D. Khamitkar, "A Comparitive Study of Removal Noise from Remote Sensing Image", IJCSI, Vol. 7, Issue. 1,No.1, January 2010
[3]. S. K. Satpathy, S. Panda, K. K. Nagwanshi, S.K. Nayak and c. Ardil, "Adaptive Non-Linear Filtering Technique for Image Restoration", IJECE 5:1 2010.
[4]. Mrs. V. Radhika and Dr. G. Padmavathi, "Performance of Various Order Statistics Filters in Impulse and Mixed Noise Removal for RS Images", SIPIJ, Vol,1, No.2, December 2010.
[5]. J.Astola and P. Kuosmanen, Fundamentals of Nonlinear Digital Filtering, Boca Raton, FL: CRC, 1997.

Remote sensing images are the images that are captured by satellites and used to retrieve information about the particular area or the object. Noise is the intrusion that occurs in the image due to which the quality of image is fouled. Noise can occur in an image due to various conditions .Salt & pepper noise is a type of noise, which appears like a mixture of black and white dots. Filters are mainly used in the editing of the noises present in the image. Median Filter is used to elimination of the noise in the image. Fuzzy C-Means algorithm is used to get a better quality image by clustering. In fuzzy c means clustering every point belongs to all clusters .In this paper Fuzzy C-Means algorithm based Knot theory is used in the colour segmentation of images for separating the water area. And the colour band is derived to get a better view of the water segmented area.

Keywords: - Remote Sensing Image, Traditional Median filter, Knot Theory, Water bodies, Clustering Algorithms

[1]. Mr. Salem Saleh Al-amir, Dr. N.V. Kalyankar and Dr. S.D. Khamitkar, "A Comparitive Study of Removal Noise from Remote Sensing Image", IJCSI, Vol.7, Issue.1,No.1,Jan 2010.
[2]. Liu, Jian Guo & Mason, Philippa J. (2009), "EssentialImage Processing for GIS and Remote Sensing", Wiley-Blackwell. p. 4. ISBN 978-0-470-51032-2.
[3]. Schowengerdt, Robert A. (2007). "Remote sensing: models and methods for image processing (3rd ed.)", Academic Press. p. 2. ISBN 978-0-12-369407-2.
[4]. Hamerly, G. and Elkan, C. (2002). ""Alternatives to the k-means algorithm that find better clusterings" Proceedings of the eleventh international conference on Information and knowledge management (CIKM).
[5]. FaberV., Mark O. Mundt, Jeffrey S. Saltzman, and James M. White (1994), Clustering and the Continuo k-Means Algorithm,Paradigm Concepts, Inc., Santa Fe, NM. Los AlamosScienceNumber 22, 138-149

Now the world is rallying digitized. To bring development in innovative world, we are ongoing on in a new concept known as the big data. Almost eighty to ninety percent of professions that are moving today seek a new and better approach to remain ambitious and profitable. To do this, big data leads them in a path that stopovers ahead of the curves. Thus big data is an advance that helps people to make their life more convenient, profitable and compatible one. Big data plays a major role in planning important vital and operational plans and instrument them.............

[1]. Neelam Singh, Neha Garg, Varsha Mittal, Data – insights, motivation and challenges, Volume 4, Issue 12, December-2013, 2172, ISSN 2229-5518 2013.
[2]. Karthik Kambatlaa, Giorgos Kollias b, Vipin Kumarc, Ananth Gramaa, Trends in big data Analytics, (2014) 74 2561–2573
[3]. Francis X. "On the Origin(s) and Development of the Term \Big Data"_ Francis X., 2012
[4]. Venkata narasimha inukollu1, sailaja arsi1 and srinivasa rao ravuri3 Security issues associated with big data in cloud computing Vol.6, No.3, May 2014
[5]. Matzat1, Ulf-Dietrich Reips2,3 1 Eindhoven "Big Data" 2012, 7 (1), 1–5 ISSN 1662-5544

Data management and decision making is one of the essential tasks in medical industry. Data mining accuracy is better in any kind of big data if the quality of the data is improved. To improve the quality of the data, preprocessing, data cleaning by error elimination, redundancy removal and store it in a standard format are some of the sequences of data-processing methods applied on the data. Almost, all of the IT companies need data processing and data mining process efficiently. One of the main task in data mining in decision making where it selects a particular constraint-based data using logical relationship. The main aim of this work is to enhance the quality of care by reducing the turnaround time of reminder and maintenance..

Keywords: - Data Cleaning, Preprocessing, Clinical Information System, Knowledge Extraction, Knowledge Acquisition.

[1]. Dick R.Steen E, Detmer DE, The Computer based patient record: An essential technology for health care, revised edition, Wasington, DC:The National Academies Press; 1997.
[2]. Kohn L, Corrigan JM, Donaldson M, eds. To err is human; building a safer health system. Washington, DC: National Academy Press; 1999
[3]. Institute of Medicine. Crossing the quality chasm; a new health system for the 21st Century. Washington, DC:National Academy Press; 2001
[4]. Aspden P, Wolcott JA, Bootman JL., et al. Preventing medication errors. Washington, DC: The National Academies Press ; 2007
[5]. Stead WW, Lin HS. Computational technology for effective health care; immediate steps and strategic directions, Washington, DC: The National Academies Press; 2009

Image can be represented in the area of digital processing, which can be told as another reproduction of an object. Segmentation is the process of partitioning a digital image into multiple regions and extracting meaningful regions for the future image analysis. The prime objective of this research work is to segment the abnormal Cancer cells in lungs. Region growing process is a simple region-based image segmentation method. Finally the boundary region of the Cancer cells is detected using region growing algorithm and various morphological operations.

Keywords: - cancer cells, segmentation, morphological operation, Region growing process.

[1]. Jian-Jiun Ding, The class of "Time-Frequency Analysis and Wavelet Transform", the Department of Electrical Engineering, National Taiwan University (NTU), Taipei, Taiwan, 2007.
[2]. Jian-Jiun Ding, The class of "Advanced Digital Signal Processing", the Department of Electrical Engineering, National Taiwan University (NTU), Taipei, Taiwan, 2008.
[3]. Amita Sharda, "Segmentation of Medical Images Based on LS-SVM using Low Level Features" International Journal Of Engineering Sciences & Research Technology, Sharda, 2(8): August, 2013, ISSN: 2277-9655 Impact Factor: 1.852
[4]. Color Image processing, " Derivation of HIS-TO-RGB and RGB-TO-HIS Conversion Equations", Gonzalez and woods, Digital Image processing,1st,ed.Addison-Wesely,1992

Internet users spend their time on social networks than search engines and other websites. Social Media networks such as Face book, Twitter, YouTube entities set up social networking pages to enhance direct interactions with the online users. Social media networks mostly depend on users for content contribution and sharing. Information is spreaded across social networks quickly and effectively to the users. Virus from the spammers could lead to personal or business loss and damage in the content. However, at the same time social media networks become admitting of different types of unwanted users or malicious spammer or hacker actions. There is a decisive need in the society or industry for security solution in social media............

Keywords: - Spam Mail, Data Mining, Social Network, Naive Bayes classifier model.

[1]. Awad, W. A., &ELseuofi, S. M. (2011). MachineLearning methods for E-mail classification.International Journal of Computer Applications,16(1).
[2]. L. Firte, C. Lemnaru, and R. Potolea,"Spam Detection Filter using KNN Algorithm and Resampling", in 6th International Conference on Intelligent Computer Communication and Processing -IEEE, pp.27-33, 2010.
[3]. Vapnik V N. Statistical learning theory [M]. John W iley &Sons, NewYork, N Y, 1998.
[4]. Ian H, Witten and Eibe Frank.Data Mining: Practical machine learning tools and techniques", 2nd Edition. San Fransisco: Morgan Kaufmann; 2005.
[5]. Caruana R.A. and Freitag D. How useful is Relevance? Technical Report [A]. AAAI Symposium on Relevance, New Orleans, 1994. [6] Blum A.L. and Langley P. Selection of Relevant.

Vehicular Ad Hoc Networks (VANET) is a part of Mobile ad hoc networks which provides a wellknown approach for Intelligent Transport System (ITS). The survey of routing protocols in VANET is essential and required for smart ITS. This paper discusses about various protocols and routing applications for VANET. This mainly focuses on advantages and disadvantages of VANET applications. It explores the impulse behind the intended, and traces the growth of these routing protocols. The comparison of various type of routing protocols are given in this paper as concluded and given below for the VANET Technology.

Keywords: - VANET, routing protocols, QoS, V2V, V2I

[1]. James Bernsen D. Mnivannan, "Unicast Routing protocols for vehicular ad hoc networks: A critical comparison and classification", in journal of Pervasive and Mobile Computing 5 (2009) 1-18
[2]. Jetzabel Serna Jesus Luna and Manel Medina "Geolocation-based Trust for Vanet‟s Privacy" Dynamic Publishers, Journal of Information Assurance and Security 4 (2009) Page 432-439, 2009.
[3]. J. Angel F. Lage, C. Pereiro Gestoso, O. Rubinos, F.Aguado Agelet "Analysis of Unicast Routing Protocols For VANETs" 2009 Fifth International Conference on Networking and Services.
[4]. Kargal, F. Papadimitratos, P. Buttyan, L. Muter, M. Schoch, E. Wiedersheim, B. Ta-Vinh Thong Calandriello,G. Held, A. Kung, A.Hubaux, J. -P. Ulm Univ., Ulm"Secure Vehicular Communication Systems:Implementation, Performance and Research Challenges",IEEE Communication Magazine, Vol. 46 issue: 11, November 2008.
[5]. T. Sawamura, K. Tanaka, M. Atajanov, N.Matsumoto, and N. Yoshida, " Adaptive Router Promotion and Group Forming in Ad hoc Networks", International Journal of Ad hoc and Ubiquitous Computing (IJAHUC), Vol 3, no 4, 2008, pp 217-223.

Now a days data outsourcing to the cloud is getting accustomed by the data owners due to enormous benefits of the scalable data storage paradigms. In these paradigms, privacy and confidentiality is the primary concerns on the outsourced data .Because it contains more sensitive information. With the emergence of sharing confidential data, several existing technique has employed using attribute based encryption and fine grained access control model with external authorities to achieve the above requirement. In addition it leads to key management issue and key escrow attacks. An empirical study of securing electronic health record through generation of access policies and automatic revocation principles is carried out in this work. Revocation becomes important due to expiration or change of user membership

Keywords: - Cloud Computing, Electronic Health Record, Access policies, Attribute Based Encryption, Multi Authority ABE

[1]. L. Ibraimi, M. Asim, and M. Petkovic, "Secure management of personal health records by applying attribute-based encryption," Technical Report, University of Twente, 2009.
[2]. S. Yu, C. Wang, K. Ren, and W. Lou, "Achieving secure, scalable, and fine-grained data access control in cloud computing," in INFOCOM, 2010, pp. 534–542.
[3]. S. Narayan, M. Gagn´e, and R. Safavi-Naini, "Privacy preserving ehr system using attribute-based infrastructure," ser. CCSW '10, 2010, pp. 47–52.
[4]. V. Goyal, O. Pandey, A. Sahai, and B. Waters, "Attribute-based encryption for fine-grained access control of encrypted data," in
ACM CCS, 2006, pp. 89–98.
[5]. M. Li, S. Yu, Y. Zheng, K. Ren, and W. Lou, "Scalable and secure sharing of personal health records in cloud computing using attribute based encryption," IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 1, pp. 131–143, 2013.