Computing on masked data: a high performance method for improving big data veracity

Abstract

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data.This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.

Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers

“…The SuperCloud is a fusion of the four large computing ecosystems: supercomputing, enterprise computing, big data and traditional databases into a coherent, unified platform. The MIT SuperCloud has spurred the development of a number of cross-ecosystem innovations in high performance databases [3], [13]; database management [19]; data protection [14]; database federation [11], [6]; data analytics [12]; dynamic virtual machines [23], [8] and system monitoring [7].…”

Toward better data veracity in mobile cloud computing: A context-aware and incentive-based reputation mechanism

“…Data veracity is becoming a research hotspot of big data and there have been many related studies in the literature [2,4,10,16,20,15]. For example, Kepner et al [10] introduced a new technique called Computing on Masked Data (CMD) to improve data veracity while allowing a wide range of computations and queries to be performed with low overhead by combining efficient cryptographic encryption methods with an associative array representation of big data.…”

“…For example, Kepner et al [10] introduced a new technique called Computing on Masked Data (CMD) to improve data veracity while allowing a wide range of computations and queries to be performed with low overhead by combining efficient cryptographic encryption methods with an associative array representation of big data. Bodnar et al [4] proposed a veracity assessment model for information dissemination on social media networks that combines natural language processing and machine learning algorithms to mine textual content generated by each user.…”

“…There are many security challenges in data veracity such as external denial-of-service, credential stealing, remote code injection, data integrity attacks, internal attacks, and supply chain attacks [10]. Consequently, the availability, confidentiality, and integrity of both the original data and the data analytics results are threatened by these attacks, e.g., the degraded availability of a big data system, the compromised confidentiality of the data and analytics, and the violated integrity of the data and analytic results.…”

Percolation Model of insider threats to assess the optimum number of rules

“…The costs of these approaches are a key aspect of their adoption and there is active research to develop technologies that minimize their limitations [Kepner et al 2014].…”