2 research outputs found
Computing on Masked Data to improve the Security of Big Data
Organizations that make use of large quantities of information require the
ability to store and process data from central locations so that the product
can be shared or distributed across a heterogeneous group of users. However,
recent events underscore the need for improving the security of data stored in
such untrusted servers or databases. Advances in cryptographic techniques and
database technologies provide the necessary security functionality but rely on
a computational model in which the cloud is used solely for storage and
retrieval. Much of big data computation and analytics make use of signal
processing fundamentals for computation. As the trend of moving data storage
and computation to the cloud increases, homeland security missions should
understand the impact of security on key signal processing kernels such as
correlation or thresholding. In this article, we propose a tool called
Computing on Masked Data (CMD), which combines advances in database
technologies and cryptographic tools to provide a low overhead mechanism to
offload certain mathematical operations securely to the cloud. This article
describes the design and development of the CMD tool.Comment: 6 pages, Accepted to IEEE HST Conferenc