Biblio

With the increasing awareness of privacy protection and data security, people’s concerns over the confidentiality of sensitive data still limit the application of distributed artificial intelligence. In fact, a new encryption form, called homomorphic encryption(HE), has achieved a balance between security and operability. In particular, one of the HE schemes named Paillier has been adopted to protect data privacy in distributed artificial intelligence. However, the massive computation of modular multiplication in Paillier greatly affects the speed of encryption and decryption. In this paper, we propose a fast CRT-Paillier scheme to accelerate its decryption process. We first introduce the Montgomery algorithm to the CRT-Paillier to improve the process of the modular exponentiation, and then compute the modular exponentiation in parallel by using OpenMP. The experimental results show that our proposed scheme has greatly heightened its decryption speed while preserving the same security level. Especially, when the key length is 4096-bit, its speed of decryption is about 148 times faster than CRT-Paillier.

The process of Big data storage has become challenging due to the expansion of extensive data; data providers will offer encrypted data and upload to Big data. However, the data exchange mechanism is unable to accommodate encrypted data. Particularly when a large number of users share the scalable data, the scalability becomes extremely limited. Using a contemporary privacy protection system to solve this issue and ensure the security of encrypted data, as well as partially homomorphic re-encryption and decryption (PHRED). This scheme has the flexibility to share data by ensuring user's privacy with partially trusted Big Data. It can access to strong unforgeable scheme it make the transmuted cipher text have public and private key verification combined identity based Augmented Homomorphic Re Encryption Decryption(AHRED) on paillier crypto System with Laplacian noise filter the performance of the data provider for privacy preserving big data.

This paper proposes a new DNA cryptographic technique based on dynamic DNA encoding and asymmetric cryptosystem to increase the level of secrecy of data. The key idea is: to split the plaintext into fixed sized chunks, to encrypt each chunk using asymmetric cryptosystem and finally to merge the ciphertext of each chunk using dynamic DNA encoding. To generate chunks, characters of the plaintext are transformed into their equivalent ASCII values and split it into finite values. Now to encrypt each chunk, asymmetric cryptosystem is applied and the ciphertext is transformed into its equivalent binary value. Then this binary value is converted into DNA bases. Finally to merge each chunk, sufficient random strings are generated. Here to settle the required number of random strings, dynamic DNA encoding is exploited which is generated using Fibonacci series. Thus the use of finite chunks, asymmetric cryptosystem, random strings and dynamic DNA encoding increases the level of security of data. To evaluate the encryption-decryption time requirement, an empirical analysis is performed employing RSA, ElGamal and Paillier cryptosystems. The proposed technique is suitable for any use of cryptography.