12,897 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
State of The Art and Hot Aspects in Cloud Data Storage Security
Along with the evolution of cloud computing and cloud storage towards matu-
rity, researchers have analyzed an increasing range of cloud computing security
aspects, data security being an important topic in this area. In this paper, we
examine the state of the art in cloud storage security through an overview of
selected peer reviewed publications. We address the question of defining cloud
storage security and its different aspects, as well as enumerate the main vec-
tors of attack on cloud storage. The reviewed papers present techniques for key
management and controlled disclosure of encrypted data in cloud storage, while
novel ideas regarding secure operations on encrypted data and methods for pro-
tection of data in fully virtualized environments provide a glimpse of the toolbox
available for securing cloud storage. Finally, new challenges such as emergent
government regulation call for solutions to problems that did not receive enough
attention in earlier stages of cloud computing, such as for example geographical
location of data. The methods presented in the papers selected for this review
represent only a small fraction of the wide research effort within cloud storage
security. Nevertheless, they serve as an indication of the diversity of problems
that are being addressed
Efficient Privacy Preserving Viola-Jones Type Object Detection via Random Base Image Representation
A cloud server spent a lot of time, energy and money to train a Viola-Jones
type object detector with high accuracy. Clients can upload their photos to the
cloud server to find objects. However, the client does not want the leakage of
the content of his/her photos. In the meanwhile, the cloud server is also
reluctant to leak any parameters of the trained object detectors. 10 years ago,
Avidan & Butman introduced Blind Vision, which is a method for securely
evaluating a Viola-Jones type object detector. Blind Vision uses standard
cryptographic tools and is painfully slow to compute, taking a couple of hours
to scan a single image. The purpose of this work is to explore an efficient
method that can speed up the process. We propose the Random Base Image (RBI)
Representation. The original image is divided into random base images. Only the
base images are submitted randomly to the cloud server. Thus, the content of
the image can not be leaked. In the meanwhile, a random vector and the secure
Millionaire protocol are leveraged to protect the parameters of the trained
object detector. The RBI makes the integral-image enable again for the great
acceleration. The experimental results reveal that our method can retain the
detection accuracy of that of the plain vision algorithm and is significantly
faster than the traditional blind vision, with only a very low probability of
the information leakage theoretically.Comment: 6 pages, 3 figures, To appear in the proceedings of the IEEE
International Conference on Multimedia and Expo (ICME), Jul 10, 2017 - Jul
14, 2017, Hong Kong, Hong Kon
Provenance Threat Modeling
Provenance systems are used to capture history metadata, applications include
ownership attribution and determining the quality of a particular data set.
Provenance systems are also used for debugging, process improvement,
understanding data proof of ownership, certification of validity, etc. The
provenance of data includes information about the processes and source data
that leads to the current representation. In this paper we study the security
risks provenance systems might be exposed to and recommend security solutions
to better protect the provenance information.Comment: 4 pages, 1 figure, conferenc
- β¦