Neural network based group authentication using (n, n) secret sharing scheme

In recent days, usage of internet is increasing so; authentication becomes the most important security services for communication purpose. Keeping this into consideration, there is need of robust security services and schemes. This paper proposes Group Authentication authenticates all users at a time belonging to the same group. The (n, n) Group Authentication Scheme is very efficient since it authenticates all users if they are group members. If they are nonmembers, then it may be used as a preprocess and apply authentication before and it identifies the non-members. Also, if any of the users present in group authentication is absent then the group is not authenticated at all, as each share is distributed to each user. It results in best authenticated system as the Group Authentication is implemented with Neural Network. So it becomes complicated for hackers to hack each neuron in a neural network. The Neural Network based group authentication is specially designed for applications performing group activities using Shamir Secret Sharing Scheme.

[1]  Hui Li,et al.  A Lossless Tagged Visual Cryptography Scheme , 2014, IEEE Signal Processing Letters.

[2]  Duncan S. Wong,et al.  On Secret Reconstruction in Secret Sharing Schemes , 2008, IEEE Transactions on Information Theory.

[3]  Achour Mostéfaoui,et al.  Low-Cost Secret-Sharing in Sensor Networks , 2010, 2010 IEEE 12th International Symposium on High Assurance Systems Engineering.

[4]  Mitsugu Iwamoto.  A Weak Security Notion for Visual Secret Sharing Schemes , 2012, IEEE Transactions on Information Forensics and Security.

[5]  Bhavani M. Thuraisingham,et al.  Secure Data Objects Replication in Data Grid , 2010, IEEE Transactions on Dependable and Secure Computing.

[6]  Lein Harn,et al.  Group Authentication , 2013, IEEE Transactions on Computers.

[7]  Xiaojiang Du,et al.  Two matrices for Blakley's secret sharing scheme , 2012, 2012 IEEE International Conference on Communications (ICC).

[8]  Wei-Ho Chung,et al.  A Probabilistic Model of $(t,n)$ Visual Cryptography Scheme With Dynamic Group , 2012, IEEE Transactions on Information Forensics and Security.

[9]  Siddharth Bali,et al.  Public key cryptography using neural networks and genetic algorithms , 2013, 2013 Sixth International Conference on Contemporary Computing (IC3).

[10]  Wael Alghamdi.  Secure data collection in wireless sensor networks , 2017 .

[11]  Chin-Chen Chang,et al.  A novel efficient (t, n) threshold proxy signature scheme , 2006, Inf. Sci..

[12]  Kamer Kaya,et al.  Threshold Cryptography Based on Blakley Secret Sharing , 2008 .

[13]  Adel A. El-Zoghabi,et al.  Survey Report on Cryptography Based on Neural Network , 2013 .

[14]  T. Godhavari,et al.  Cryptography Using Neural Network , 2005, 2005 Annual IEEE India Conference - Indicon.

[15]  B TRajendraA,et al.  A New Approach to Analyze Visual Secret Sharing Schemes For Biometric Authentication-A Survey , 2013, FOCS 2013.

[16]  Tai-Wen Yue,et al.  A Neural-Network Approach for Visual Cryptography and Authorization , 2004, HIS.

[17]  Huy Hoang Ngo,et al.  Dynamic Key Cryptography and Applications , 2010, Int. J. Netw. Secur..

[18]  Donghui Guo,et al.  Security Analysis of Public-key Encryption Scheme Based on Neural Networks and Its Implementing , 2006, 2006 International Conference on Computational Intelligence and Security.

[19]  Tai-Wen Yue,et al.  A neural network approach for visual cryptography , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.