Volume-07 , Special Issue-12 , May - 2019 Go Back

Cloud computing , a remote service platform used to store information and execute applications. It lets you run services and data online by request via a simple internet connection. Cloud computing is a relatively new technology that will become more widespread. The adoption of this technology is not without its challenges and risks. With the cloud model, you lose control over physical security. In a public cloud, you are sharing computing resources with other companies. In a shared pool outside the enterprise, you don’t have any knowledge or control of where the resources run. Exposing your data in an environment shared with other companies could give the government “reasonable cause” to seize your assets because another company has violated the law. Simply because you share the environment in the cloud, may put your data at risk of seizure. Storage services provided by one cloud vendor may be incompatible with another vendor’s services should you decide to move from one to the other. Vendors are known for creating what the hosting world calls “sticky services”—services that an end user may have difficulty transporting from one cloud vendor to another (e.g., Amazon’s “Simple Storage Service” [S3] is incompatible with IBM’s Blue Cloud, or Google, or Dell). This study aims to review and classify the issues that surround the implementation of cloud computing which a hot area that needs to be addressed by future research.

Cloud Computing, security issues in cloud computing

[1] V.S. Varnika, “ Cloud Computing Advantages and Challenges for Developing Nations”, International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.3, pp.51-55 , June (2018)
[2] Ramona Carr, “Top Challenges in Cloud Security”, 2018
[3] Naresh vurukonda, B.Thirumala Rao, “A Study on Data Storage Security Issues in Cloud Computing”,2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-2016).
[4] Ilango Sriram, Ali Khajeh-Hosseini, “Research Agenda in Cloud Technologies”.
[5] John W. Rittinghouse and James F. Ransome, “Cloud Computing, Implementation, Management, and Security”, © 2010 by Taylor and Francis Group, LLC.
[6] Dr. Ramalingam Sugumar, K.Raja, “A Study on Enhancing Data Security in Cloud Computing Environment”, Dr. Ramalingam Sugumar et al, International Journal of Computer Science and Mobile Applications, Vol.6 Issue. 3, March- 2018, pg. 44-49.
[7] Prof. Syed Neha Samreen, Prof. Neha Khatri-Valmik, Prof. Supriya Madhukar Salve, Mr. Pathan Nouman Khan,“Introduction to Cloud Computing”, International Research Journal of Engineering and Technology (IRJET), Volume: 05 Issue: 02 | Feb-2018.
[8] Qusay Kanaan Kadhim , Robiah Yusof , Hamid Sadeq Mahdi, Sayed Samer Ali Al-shami , Siti Rahayu Selamat, “A Review Study on Cloud Computing Issues”, 1st International Conference on Big Data and Cloud Computing (ICoBiC) 2017 .
[9] Everaldo Aguiar, Yihua Zhang, and Marina Blanton,” An Overview of Issues and Recent Developments in Cloud Computing and Storage Security”.
[10] M.B. Jayalekshmi and S.H. Krishnaveni, “A Study of Data Storage Security Issues in Cloud Computing”, Indian Journal of Science and Techonology, Vol 8(24), DOI:10.17485/ijst/2015/v8i24/84229, September 2015.

Nayana Bnasod, Pranjal Dhore, Nisha Balani, "A Study on Data Storage Security Issues in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.71-74, 2019.

As per recent trend, cloud computing is the most high in demand, reliable and efficient technology. There are amazing benefits that are available with cloud computing like minimum investment cost, tremendous storage space, virtualization, resource sharing. The Cloud users are beneficial with this technology as they can use the large volume of data in cloud and access it anytime, anywhere easily from any corner of the world. This is achievable in pay per use or pre-paid basis. As this is a pro it also has cons, i.e security and Market competition. Many users can request for data, so, technologist need to ensure they authenticate and authorize the users to access this data. This is achievable thru access control schemes which not only allows a authenticated and authorized user to access the data but also can deny access to malicious user. In this paper we are discussing the same pain area i.e existing access control schemes along with Analysis and techniques to improve the speed of data access.

Cloud Computing, data access, compression techniques

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Nikita Ashtankar, Mona Mulchandani, "Statistical Analysis of Data Access Techniques in Cloud", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.75-77, 2019.

Cryptography or cryptology is the practice and study of techniques for secure communication in the presence of third parties called adversaries. Cloud computing provides a flexible and convenient way for data sharing, which brings various benefits for both the society and individuals. But there exists a natural resistance for users to directly outsource the shared data to the cloud server since the data often contain valuable information. Thus, it is necessary to place cryptographically enhanced access control on the shared data. Identity-based encryption is a promising cryptographical primitive to build a practical data sharing system. However, access control is not static. That is, when some user’s authorization is expired, there should be a mechanism that can remove him/her from the system. Consequently, the revoked user cannot access both the previously and subsequently shared data. To this end, we propose a notion called revocable-storage identity-based encryption (RS-IBE), which can provide the forward/backward security of ciphertext by introducing the functionalities of user revocation and ciphertext update simultaneously. Furthermore, we present a concrete construction of RS-IBE, and prove its security in the defined security model. he performance comparisons indicate that the proposed RS-IBE scheme has advantages in terms of functionality and efficiency, and thus is feasible for a practical and cost-effective data-sharing system. Finally, we provide implementation results of the proposed scheme to demonstrate its practicability. Further we will use cryptography in authentication process so as to authenticated person only could share data.

Revocable Storage Identity-Based Encryption,Cloud data security, Authentication

[1] Shamir, “Identity-based cryptosystems and signature schemes,” in Advances in cryptology. Springer, 1985, pp. 47–53.
[2] iCloud. (2014) Apple storage service. [Online]. Available:https://www.icloud.com/
[3] Azure. (2014) Azure storage service. [Online]. Available: http://www.windowsazure.com/
[4] Amazon. (2014) Amazon simple storage service (amazon s3). [Online]. Available: http://aws.amazon.com/s3/
[5] K. Chard, K. Bubendorfer, S. Caton, and O. F. Rana, “Social cloud computing: A vision for socially motivated resource sharing,” Services Computing, IEEE Transactions on, vol. 5, no. 4, pp. 551–563, 2012.
[6] C. Wang, S. S. Chow, Q. Wang, K. Ren, and W. Lou, “Privacy preserving
public auditing for secure cloud storage,” Computers, IEEE Transactions on, vol. 62, no. 2, pp. 362–375, 2013.
[7] G. Anthes, “Security in the cloud,” Communications of the ACM, vol. 53, no. 11, pp. 16–18, 2010.
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[9] J. H. Seo and K. Emura, “Revocable identity-based encryption revisited: Security model and construction,” in Public-Key Cryptography–PKC 2013. Springer, 2013, pp. 216–234.
[10] L. M. Vaquero, L. Rodero-Merino, J. Caceres, and M. Lindner, “A break in the clouds: towards a cloud definition,” ACM SIGCOMM Computer Communication Review, vol. 39, no. 1, pp. 50–55, 2008.
[11] X. Huang, J. Liu, S. Tang, Y. Xiang, K. Liang, L. Xu, and J. Zhou, “Cost-effective authentic and anonymous data sharing with forward security,” Computers, IEEE Transactions on, 2014, doi: 10.1109/TC.2014.2315619.

Nikita Daudkar, Pranjal Dhore, Nisha Balani, "Cloud Data Security Authentication and Data Sharing Using Revocable-Storage Identity-Based Encryption", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.78-80, 2019.

A face annotation has many applications the main part of based face annotation is to management of most same facial images and their weak data labels. This problem different method are adopted. The efficiency of annotating systems are improved by using these methods. This paper proposes a review on various techniques used for detection and analysis of each technique. Combine techniques are used in retrieving facial images based on query. So it is effective to label the images with their exact names. The detected face recognition techniques can annotate the faces with exact data labels which will help to improve the detection more efficiently. For a set of semantically similar images Annotations from them. Then content-based search is performed on this set to retrieve visually similar images, annotations are mined from the data descriptions. The method is to find the face data association in images with data label. Specifically, the task of face-name association should obey the constraint face can be a data appearing in its associated a name can be given to at most one face and a face can be assigned to one name.

Annotation, weak data, exact data, detection, Content Based

[1] Mining Weakly Labeled Web Facial Images for Search-Based Face Annotation Dayong Wang, Steven C.H. Hoi, Member, IEEE, Ying He,
and Jianke Zhu JANUARY 2017.
[2] Dayong Wang, Steven C.H. Hoi, Ying He, and Jianke Zhu,”
Mining Weakly Labeled Web Facial Images for Search-Based
Face Annotation” IEEE Transactions on Knowledge and Data
Engineering, vol. 26, no. 1, January 2014
[3] D. Wang, S.C.H. Hoi, Y. He, and J. Zhu, “Retrieval-Based Face Annotation by Weak Label Regularized Local Coordinate Coding,” Proc. 19th ACM Int’l Conf. Multimedia (Multimedia), pp. 353-362, 2011.
[4] W. Dong, Z. Wang, W. Josephson, M. Charikar, and K. Li, “Modeling LSH for Performance Tuning,” Proc. 17th ACM Conf. Information and Knowledge Management (CIKM), pp. 669-678, 2008
[5] C. Siagian and L. Itti, “Rapid Biologically-Inspired Scene Classification Using Features Shared with Visual Attention,”IEEE Trans. Pattern Analysis and Machine Intelligence, vol.29, no. 2,pp. 300-312, Feb. 2007.
[6] Y. Tian, W. Liu, R. Xiao, F. Wen, and X. Tang, “A Face Annotation Framework with Partial Clustering and Interactive Labeling,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2007.
[7] X.-J. Wang, L. Zhang, F. Jing, and W.-Y. Ma, “AnnoSearch: Image Auto-Annotation by Search,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1483- 1490, 2006.
[8] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Survey, vol. 35, 2003.
[9] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.

Pankaj S Wankhede, Sachin Choudhari, Ashish Kumbhare, "Fake Data Mining over Distributed Database With Face Annotation", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.81-84, 2019.

In India Language Translation systems have been developed for translation from English to Indian Languages and from regional languages to regional languages. These systems are also used for teaching machine translation to the students and researchers. Most of these systems are in the English to Hindi domain with exceptions of a Hindi to English and English to Kannada machine translation system. English is a SVO (subject-verb-object) language while Indian regional languages are SOV (subject-object-verb) and are relatively of free word-order. A survey of the machine translation systems that have been developed in media for translation from English to Indian languages and among Indian languages reveals that the machine translation software is used in field testing or is available as web translation service. We present an analysis regarding the performance of the state-of-art Phrase-based Language Translation (LT) on Indian Sindhi languages. We report baseline systems on Sindhi language pairs. The motivation of this study is to promote the development of SLT and linguistic resources for these language pairs, as the current state-of-the-art is quite bleak due to sparse data resources. The success of an SLT system is contingent on the availability of a large parallel corpus i.e. Dictionary. Such data is necessary to reliably estimate translation probabilities.

Statistical Language Translation (SLT), Phrase-based Translation, Parallel Corpus, Natural Language Processing (NLP), Sindhi Phrase Word By Word

[1] NadeemJadoon Khan, Waqas Anwar & Nadir Durrani, “Machine Translation Approaches and Survey for Indian Languages”,2017.
[2] ALPAC “Language and Machines: Computers in Translation and Linguistics”.A report by the Automatic Language Processing Advisory Committee (Tech. Rep. No. Publication 1416), 2101 Constitution Avenue, Washington D.C., 20418 USA: National Academy of Sciences, National Research Council, 1966.
[3]Balajapally, P., Bandaru, P., Ganapathiraju, M., Balakrishnan, N., & Reddy, R., “Multilingual Book Reader: Transliteration, Word-to-Word Translation and Full-text Translation”, 2006.
[4] Dwivedi, S. K., &Sukhadeve, P. P.,“Machine Translation System in Indian Perspectives”,Journal of Computer Science, 6(10), 1111-1116, 2010.
[5] Antony P. J.,“Machine Translation Approaches and Survey for Indian Languages. Computational Linguistics and Chinese Language Processing”, Vol. 18, No. 1, March 2013, pp. 47-78.
[6] Hasler, E., Haddow B., and Koehn, P.,“Sparse lexicalised features and topic adaptation for SMT”,In Proceedings of the seventh International Workshop on Spoken Language Translation, pages 268–275, 2012.
[7] Och, F., “A systematic comparison of various statistical alignment models. Computational Linguistics”, 29(1):19–5, 2003.
[8] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu, “BLEU: a Method for Automatic Evaluation of Machine Translation”, In Proceedings of 40th Annual meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002, pp. 311–318.
[9] Bisazza, A. and Federico, M., “Chunk-based verb reordering in VSO sentences for Arabic-English statistical machine translation”, In Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and Metrics MATR, WMT ’10,15-16 july 2010, pp. 235–243.
[10] J. Eisner, “Learning non-isomorphic tree mappings for machine translation”, In Proceedings of the ACL Interactive Poster/Demonstration Sessions, 2003, 205–208.
[11] Dash, NiladriSekhar, Chaudhuri, BidyutBaran, “Why do we need to develop corpora in Indian languages?” A paper presented at SCALLA 2001 conference, Bangalore.
[12] Rao, Durgesh, “Machine Translation in India: A Brief Survey”, SCALLA 2001 conference, Bangalore.
[13] Naskar, S., &Bandyopadhyay, S,“Use of Machine Translation in India: Current Status”, In Proceedings of MT SUMMIT X; September 13-15, 2005, Phuket, Thailand.
[14] Bandyopadhyay S., “ANUBAAD - The Translator from English to Indian Languages” In proceedings of the VIIth State Science and Technology Congress, Calcutta. India, 2000, pp. 43-51.

Pinky Gangwani, Samir Ajani, "Language Translator using Dictionary and APIs from English to Sindhi", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.85-88, 2019.

This paper features a software system called ∞Exams (InfinityExams) which supports (primarily in higher education) paper-based examination and makes it easier, more comfortable and speeds up the whole process while keeping every single positive attribute of it but also reducing the number of negative aspects. The approach significantly differs from the ones used in the previous 10+ years which were implemented in such a way that they could not reproduce and replace the traditional paper-based examination model. The heart of the article relies on the most important element of the software which is the image processing flow.

E-assessment, computer-based assessment, computer-assisted assessment, computer-aided assessment, examination, exam, image processing

[1] Davis, Michelle R. "Online Testing Suffers Setbacks in Multiple States." Education Week 32.30 (2013): 1-18.
[2] Istvan Vajda, "Computer Aided Teaching of Discrete Mathematics and Linear Algebra", University of Debrecen, PhD Thesis (2012).
[3] Csink, L., Gyorgy, A., Raincsak, Z., Schmuck, B., Sima, D., Sziklai, Z., & Szoll˝osi, S. "Intelligent assessment systems for e-learning." Proc. of the 4-th European Conference on E-Activities, ECOMM-LINE 2003. (2003).
[4] Gyorgy, A., & Vajda, I. "Intelligent mathematics assessment in eMax." AFRICON 2007. IEEE (2007).
[5] Sima, D., Schmuck, B., Szoll˝osi, S., & Miklos, A. "Intelligent short text assessment in eMax." Towards intelligent engineering and information technology. Springer Berlin Heidelberg (2009): 435-445. 000304 Á. Tóth et al. • E-assessment using Image Processing in & infin;Exams
[6] Keady, G., Fitz-Gerald, G., Gamble, G., & Sangwin, C. "Computer-aided assessment in mathematical sciences." Proceedings of The Australian Conference on Science and Mathematics Education (formerly UniServe Science Conference). (2012).
[7] Hendriks, Remco. "Automatic exam correction." UVA Universiteit van Amsterdam (2012).
[8] de Assis Zampirolli, Francisco, Jose Artur Quilici Gonzalez, and Rogerio Perino de Oliveira Neves. "Automatic Correction of Multiple-Choice Tests using Digital Cameras and Image Processing." Universidade Federal do ABC (2010).
[9] Llamas-Nistal, M., Fernandez-Iglesias, M. J., Gonzalez- Tato, J., & Mikic-Fonte, F. A. "Blended e-assessment: Migrating classical exams to the digital world." Computers & Education 62 (2013): 72-87.
[10] Duda, Richard O., and Peter E. Hart. "Use of the Hough transformation to detect lines and curves in pictures." Communications of the ACM 15.1 (1972): 11-15.
[11] Otsu, Nobuyuki. "A threshold selection method from gray-level histograms." Automatica 11.285-296 (1975): 23-27.
[12] Soille, Pierre. "On morphological operators based on rank filters." Pattern recognition 35.2 (2002): 527-535.
[13] Deodhare, Dipti, NNR Ranga Suri, and R. Amit. "Preprocessing and Image Enhancement Algorithms for a Form-based Intelligent Character Recognition System." IJCSA 2.2 (2005): 131-144.
[14] Eikvil, Line. "Optical character recognition." citeseer. ist.psu.edu/142042.html (1993).

Prajakta Pathe, Sachin Choudhari, Monali Gulhane, "E-assessment Using Image Processing in ∞Exams", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.89-93, 2019.

Handwritten content recognition is the skill to transliterate the text input encased in reports or pictures into digitally advanced content. The content example can change from dialect to dialect. Human composed content includes a wide arrangement of varieties, for instance, couple of languages have characters segregated from one another while a couple of languages incorporate cursive organizations. Along these lines, making it profoundly difficult to precisely recognize transcribed contents. Customarily, recognizing transcribed contents was done through character segmentation, feature extraction, or character acknowledgment. With changing occasions and developing innovations, neural networks - a machine learning approach has helped in characterizing and grouping transcribed messages massively. This paper tries to decipher a person`s manually written content to computerized organize utilizing a neural system approach. Simulating a neural network to recognize written by hand content would help in accomplishing unrivalled exactness, and make an enhanced and quick calculation. The cutting-edge approaches focus on extracting features by eliminating distortions in addition to the commotion, and later anticipate the conceivable outcomes of that specific character. The way toward recognizing written by hand message has been distinguished as one of the high-flying tests in the field of characteristic natural language processing, machine learning, and computer vision applications.

Handwritten Text Recognition, machine learning, neural network, image recognition

[1] K. Peymani, M. Soryani, “From machine generated to handwritten character recognition; a deep learning approach”, IEEE 2017 3rd International Conference on Pattern Recognition and Image Analysis (IPRIA),2017.
[2] Meiyin Wu, Li Chen, “An Image recognition based on deep learning”, 2015 Chinese Automation Congress, 2015
[3] Mahmoud M. Abu Ghosh, A. Y. Maghari, “A Comparative Study on Handwritten Digit Recognition using Neural Network”, 2017 International Conference on Promising Electronic Technologies, 2017
[4] M. Rajnoha, R. Burget, M. K. Dutta, “Handwritten Comenia Script Recognition with Convolutional Neural Network”, IEEE 2017 40th International Conference on Telecommunication and Signal Processing, 2017.
[5] T.K Das, A. Tripathy and A. Mishra, “Optical Character Recognition using Artificial Neural Network”, International Conference on Computer Communication and Information (ICCCI-2017), 2017.
[6] Md Zahangir Alom, P. Sidike, M. Hasan, T. M.Tanha and V. K. Ansari, “Handwritten Bangla Character Recognition using State-of-the-Art Deep Convolutional Neural Networks”, Hindawi Computational Intelligence, and Nanoscience, Vol. 2018, 2018.
[7] S. Kumar, K. Kumar, R. Mishra, “Scene Text Recognition using Artifical Neural Network: A survey”, International Journal of Computer Applications, March 2016.
[8] J. Puigcerver, “Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?”,14th IAPR International Conference on Document Analysis and Recognition, 2017.
[9] S. Ansari, U. Sutar, “Devanagari Handwritten Character Recognition using Hybrid Features Extraction and Feed Forward Neural Network Classifier”, International Journal of Computer Applications, Nov. 2015.
[10] N Prameela, P Anjusha, R Kartik, “Offline Telugu handwritten characters recognition using OCR”, International Conference on Electronics, Communication and Aerospace Technology, 2017.

Pranati Paidipati, Sachin Choudhari, Ashish Kumbhare, "Recognition of Handwritten Text Using Neural Network Approach: A Complete Study", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.94-96, 2019.

With the rapid development of network bandwidth, the volume of user’s data is rising geometrically. User’s requirement cannot be satisfied by the capacity of local machine any more. Therefore, people try to find new methods to store their data. Pursuing more powerful storage capacity, a growing number of users select cloud storage. Storing data on a public cloud server is a trend in the future and the cloud storage technology will become wide spread in a few years. Cloud storage is a cloud computing system which provides data storage and management service. With a cluster of applications, network technology and distributed file system technology, cloud storage makes a large number of different storage devices work together coordinately. Nowadays there are a lot of companies providing a variety of cloud storage services, such as Dropbox, Google Drive, iCloud, Amazon Web Services, etc. These companies provide large capacity of storage and various services related to other popular applications, which in turn leads to their success in attracting humorous subscribers. However, cloud storage service still exists a lot of security problems. The privacy problem is particularly significant among those security issues. In history, there were some famous cloud storage privacy leakage events. For example, in 2018 despite a robust legislation on data protection by UIDAI, Aadhaar numbers and bank details of over 134,000 beneficiaries on Andhra Pradesh Housing Corporation`s website have been leaked. Apples iCloud leakage event in 2014, numerous Hollywood actresses private photos stored in the clouds were stolen. This event caused an uproar, which was responsible for the users’ anxiety about the privacy of their data stored in cloud server.

UIDAI (Unique Identification Authority of India), Cloud Computing, Cloud Storage, Anonymity

[1] A.Murugan and R.Thilagavathy,” Securing Cloud Data using DNA and Morse code: A Triple Encryption Scheme”, International Journal of Control Theory and Applications (IJCTA), vol.10, pp.31-18, Nov 2017.
[2] Jacob Grasha, and A. Murugan, ”A Hybrid Encryption Scheme using DNA Technology” The International Journal of Computer Science and Communication Security (IJCSCS), vol.3 (2), pp.61-65, Feb 2013.
[3] G. Feng, “A data privacy protection scheme of cloud storage,” vol. 14, no. 12, pp. 174–176, 2015

Puransingh Chauhan, Sachin Chaudhari, Rana Syeda, "A Three-Layer Privacy Preserving Cloud Storage Scheme Based on DNA Computing with Morse code and Zigzag Pattern", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.97-100, 2019.

Disruption-tolerant network (DTN) technologies are becoming successful solutions that allow wireless devices carried by soldiers to communicate with each other and access the confidential information or command reliably by exploiting external storage nodes. Some of the most challenging issues in this scenario are the enforcement of authorization policies and the policies update for secure data retrieval. However, the problem of applying CP-ABE in decentralized DTNs introduces several security and privacy challenges with regard to the attribute revocation, key escrow, and coordination of attributes issued from different authorities. We demonstrate how to apply the proposed mechanism to securely and efficiently manage the confidential data distributed in the disruption-tolerant military network.

Internet of Things, machine learning, cloud data, forecasting, load

[1] J. Burgess, B. Gallagher, D. Jensen, and B. N. Levine, “Maxprop:Routing for vehicle-based disruption tolerant networks,” in Proc.IEEE INFOCOM, 2006, pp. 1–11.
[2] M. Chuah and P. Yang, “Node density-based adaptive routing scheme for disruption tolerant networks,” in Proc. IEEE MILCOM, 2006, pp.
[3] M. M. B. Tariq, M. Ammar, and E. Zequra, “Mesage ferry route design for sparse ad hoc networks with mobile nodes,” in Proc. ACM
[4] S. Roy andM. Chuah, “Secure data retrieval based on ciphertext policy attribute-based encryption (CP-ABE) system for the DTNs,” Lehigh CSE Tech. Rep., 2009.
[5] M. Chuah and P. Yang, “Performance evaluation of content-based information retrieval schemes for DTNs,” in Proc. IEEE MILCOM,
[6] M. Kallahalla, E. Riedel, R. Swaminathan, Q. Wang, and K. Fu, “Plutus: Scalable secure file sharing on untrusted storage,” in Proc. Conf. File Storage Technol., 2003, pp. 29–42.
[7]. Wang, L.M.; Shi, Y. Patrol detection for replica attacks on wireless sensor networks. Sensors 2011, 11, 2496–2504.
[8]. Zhu, S.; Setia, S.; Jajodia, S.; Ning, P.Interleaved hop-by-hop authentication against false data injection attacks in sensor networks. ACM Trans. Sensor Netw. 2007.

Pushpraj Deshkar, Amit Pampatwar, Raana Syeda, "Secured Data Retrieval Disruption Tolerant Network", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.101-104, 2019.

To perk up the security as well as the quality of decrypted, recovered images, this paper proposes a classification permutation based separable-reversible data hiding in encrypted images. A classification permutation encryption in combination with the XOR-encryption is initially designed to advance the privacy of both pixel-value and pixel location. Then, each bit in the further data is embedded in the most significant bit (MSB) of the encrypted pixels which are indiscriminately chosen based on the data-hiding key in the smooth set. The quality of decrypted and recovered image can be significantly improved by exploiting the spatial correlation as the MSBs of smooth pixels are used to embed the additional data. Results of experimentation exhibit that the quality of decrypted image obtained by the proposed method is superior than to the existing methods. There is 100% probability for lossless recovery from proposed method.

separable-reversible data hiding, encrypted images, RDH-EI, classification permutation.

[1] F. Huang, J. Huang and Y. Shi, “New framework for reversible data hiding in encrypted domain,” IEEE Trans. Inf. Forensics Security, vol. 11, no. 12, pp. 2777-2789, Dec. 2016.
[2] X. Zhang, “Reversible data hiding in encrypted image,” IEEE Signal Process. Lett., vol. 18, no. 4, pp. 255-258, April. 2011.
[3] W. Hong, T. Chen and H. Wu, “An improved reversible data hiding in encrypted images using side match,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 199-202, April. 2012.
[4] X. Liao and C. Shu. “Reversible data hiding in encrypted images based on absolute mean difference of neighboring pixels,” J. Vis. Commun. Image Represent., vol. 28, pp. 21-27, 2015.
[5] C. Qian and X. Zhang. “Effective reversible data hiding in encrypted image with privacy protection for image content,” J. Vis. Commun. Image Represent., vol. 31, pp. 154-164, 2015.
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Reysh Chandrikapure, Pranjal Dhore, Nisha Balani, "Separable Reversible Data Hiding using XOR and Permutation Encryption in Image", International Journal of Computer Sciences and Engineering, Vol.07, Issue.12, pp.105-110, 2019.