Cryptography for big data environments: Current status, challenges, and opportunities. Issue 1 (5th January 2020)

• Record Type: Journal Article
• Authors: Rabanal, Fernando; Martínez, Consuelo
• Is Part Of: Computational and mathematical methods. Volume 2:Issue 1(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-05
• Subjects: big data -- fully‐homomorphic encryption -- neural cryptography -- order‐preserving encryption; Mathematics -- Data processing -- Periodicals; Numerical analysis -- Periodicals; Numerical analysis; Mathematics -- Data processing; Periodicals; 004.0151
• DOI: 10.1002/cmm4.1075
• Languages: English
• ISSNs: 2577-7408

Abstract

Abstract : Big data environments have become a standard solution in most public and private corporations since they allow the acquisition and processing of massive volumes of heterogeneous data, and also, act as enablers to extract useful information and insights from this data to optimize internal and external operations in these businesses. As big data tools evolve and become commodities, their democratization process helped promote new industries, business models, companies, and all sorts of new features to improve our way of life. On the other hand, the demand for flexible and powerful privacy schemes for big data has also increased, and it is now an active area of research, with different approaches to the initial problem being taken until now. In this document, we will review some of the most notorious ones, such as trying to preserve various mathematical properties in ciphertexts or using neural network‐based solutions for different parts of the encryption process, allowing interesting features in the cryptographic scheme by construction. Privacy individual and social concerns of potential misuses of big data, as the primary root cause for this demand, also pose an opportunity for Cryptography to propose adaptation of standard solutions, as well as new, tailored ones for these environments. The latter should allow the proposals to tackle the specific needs of each individual big data application while addressing privacy issues in a standardized way. Finally, though it is usually considered that cryptographic schemes for big data environments are inherently resource intensive by construction, it can be seen that there are clear opportunities for efficiency improvements in current solutions for different tasks that do not require complex algorithms to be applied over encrypted space. In this document, we discuss and evaluate potential improvements in some cryptographic schemes for various tasks of different nature, considering the implications over big data setups, and deriving some open questions and possible research directions on different fields of interest.