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.

to appear in IEEE High Performance Extreme Computing 2014 (ieee-hpec.org)

References in corpus (1)

• D4M 2.0 Schema: A General Purpose High Performance Schema for the Accumulo Database

Cited by in corpus (12)

• Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysis
• Graphs, Matrices, and the GraphBLAS: Seven Good Reasons
• Measuring the Impact of Spectre and Meltdown
• LLMapReduce: Multi-Level Map-Reduce for High Performance Data Analysis
• Computing on Masked Data to improve the Security of Big Data
• Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor
• Node-Based Job Scheduling for Large Scale Simulations of Short Running Jobs
• Lessons Learned from a Decade of Providing Interactive, On-Demand High Performance Computing to Scientists and Engineers
• Best of Both Worlds: High Performance Interactive and Batch Launching
• Percolation Model of Insider Threats to Assess the Optimum Number of Rules
• Parallel Vectorized Algebraic AES in MATLAB for Rapid Prototyping of Encrypted Sensor Processing Algorithms and Database Analytics
• Securing HPC using Federated Authentication