Ring-Learning with Errors Cryptography

2020 ◽  
pp. 343-357
Author(s):  
Marius Iulian Mihailescu ◽  
Stefania Loredana Nita
Keyword(s):  
Learning With Errors

Middle-Product Learning with Errors

2017 ◽  
pp. 283-297 ◽  
Author(s):  
Miruna Roşca ◽  
Amin Sakzad ◽  
Damien Stehlé ◽  
Ron Steinfeld
Keyword(s):  
Learning With Errors

scholarly journals Balancing Access Control and Privacy for Data Deduplication via Functional Encryption

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bo Mi ◽  
Ping Long ◽  
Yang Liu ◽  
Fengtian Kuang
Keyword(s):  
Access Control ◽  
Security Analysis ◽  
Functional Encryption ◽  
Data Deduplication ◽  
Paper Briefly ◽  
Storage Security ◽  
Redundancy Removal ◽  
Proof Of Ownership ◽  
Learning With Errors ◽  
And Storage

Data deduplication serves as an effective way to optimize the storage occupation and the bandwidth consumption over clouds. As for the security of deduplication mechanism, users’ privacy and accessibility are of utmost concern since data are outsourced. However, the functionality of redundancy removal and the indistinguishability of deduplication labels are naturally incompatible, which bring about a lot of threats on data security. Besides, the access control of sharing copies may lead to infringement on users’ attributes and cumbersome query overheads. To balance the usability with the confidentiality of deduplication labels and securely realize an elaborate access structure, a novel data deduplication scheme is proposed in this paper. Briefly speaking, we drew support from learning with errors (LWE) to make sure that the deduplication labels are only differentiable during the duplication check process. Instead of authority matching, the proof of ownership (PoW) is then implemented under the paradigm of inner production. Since the deduplication label is light-weighted and the inner production is easy to carry out, our scheme is more efficient in terms of computation and storage. Security analysis also indicated that the deduplication labels are distinguishable only for duplication check, and the probability of falsifying a valid ownership is negligible.

MPSign: A Signature from Small-Secret Middle-Product Learning with Errors

2020 ◽  
pp. 66-93
Author(s):  
Shi Bai ◽  
Dipayan Das ◽  
Ryo Hiromasa ◽  
Miruna Rosca ◽  
Amin Sakzad ◽  
...  
Keyword(s):  
Learning With Errors

Learning with Errors in the Exponent

2016 ◽  
pp. 69-84
Author(s):  
Özgür Dagdelen ◽  
Sebastian Gajek ◽  
Florian Göpfert
Keyword(s):  
Learning With Errors

scholarly journals Proxy Re-Encryption in cloud using ALBC (adaptive lattice based cryptography)

2019 ◽  
Vol 16 (3) ◽  
pp. 1455
Author(s):  
Chandrakala B M ◽  
S C Lingareddy
Keyword(s):  
Comparative Analysis ◽  
Data Sharing ◽  
Data Security ◽  
Key Generation ◽  
Encryption Scheme ◽  
Two Phase ◽  
Data Store ◽  
Learning With Errors ◽  
Lattice Based Cryptography

<p>In recent days, data sharing has provided the flexibility to share the data, store the data, and perform operation on data virtually as well as cost effectively. Data sharing in cloud is one of the feature, which is being popular and widely accepted. However, the concern here is to ensure the data security and this has led the researcher to research in this area. To provide the security several Proxy re-encryption scheme has been introduced, however all these method lacks of efficiency. Hence In this paper, we propose a scheme known as ALBC (Adaptive Lattice Based Cryptography), this scheme follows the two phase i.e. encryption and Re-encryption. Encryption phase has few algorithms such as Key_Gen, Enc, Dec. Similarly ALBC Re-Enc has five algorithm i.e. Key_Gen, Key_ReGen,  Enc, Re-Enc, Dec. our algorithm not only provides the security but also solves the problem of RL(Ring-learning) with errors problems. In order to evaluate, our algorithm is compared with the existing model in terms of encryption time, decryption time, re-encryption time, key generation  and key regeneration by varying the various key size. When we observe the comparative analysis, it is observed that our algorithm outperforms the existing algorithm.</p>

scholarly journals Learning with Errors and Extrapolated Dihedral Cosets

2018 ◽  
pp. 702-727 ◽  
Author(s):  
Zvika Brakerski ◽  
Elena Kirshanova ◽  
Damien Stehlé ◽  
Weiqiang Wen
Keyword(s):  
Learning With Errors

Compact Ring Signatures from Learning with Errors

2021 ◽  
pp. 282-312
Author(s):  
Rohit Chatterjee ◽  
Sanjam Garg ◽  
Mohammad Hajiabadi ◽  
Dakshita Khurana ◽  
Xiao Liang ◽  
...  
Keyword(s):  
Compact Ring ◽  
Ring Signatures ◽  
Learning With Errors
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