Big Privacy: Challenges and Opportunities of Privacy Study in the Age of Big Data

Abstract

One of the biggest concerns of big data is privacy. However, the study on big data privacy is still at a very early stage. We believe the forthcoming solutions and theories of big data privacy root from the in place research output of the privacy discipline. Motivated by these factors, we extensively survey the existing research outputs and achievements of the privacy field in both application and theoretical angles, aiming to pave a solid starting ground for interested readers to address the challenges in the big data case. We firstly present an overview of the battle ground by defining the roles and operations of privacy systems. Secondly, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Thirdly, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourthly, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of the article, hoping to shed light on the exciting and almost uncharted land.

A Survey on Deep Learning Techniques for Big Data in Biometrics

“…Then the milestones of the current two major research categories of privacy such as data clustering and privacy frameworks are reviewed. After that, the author discusses the effort of privacy study from the perspectives of different disciplines [8].…”

“…According to the author Shui Yu [8], the biggest concern of big data is privacy. In this paper, the author extensively surveys the existing research outputs and achievements of the privacy field in both application and theoretical angles.…”

Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

“…Privacy research to find a compromise is ongoing. In [16], the existing privacy systems, research frameworks, several privacy disciplines and mathematical representations, including challenges and opportunities with respect to privacy, were elaborated. Thus, the trade-off between two influential factors should be well managed in a valid manner, such as the design of privacy-enhancing machine learning systems [15].…”

“…The two networks are simultaneously trained in an adversarial manner, essentially via a two-player min-max game. The objective of the game is min max ~ ( ) ( ) + ~ ( ) log(1 − ( )) (16) When the discriminator cannot distinguish the virtual examples from the training examples, the training process is complete. The ultimate generator corresponds to a probability distribution p , which well approximates the training data distribution p with the existence of global optimal solution in optimizing objective (16) for p = , as proved in [45,46].…”

“…However, these advanced data accessing techniques inspire another direction that presses us to take big data seriously. We enjoy the convenience of and harvest other economic value from big data, but simultaneously, sensors and analytic tools seem to invade our lives and privacy [5] [15,16] [12]. Private personal information in the form of instantaneous locations, personal preferences, and recent interests are easier to access with mobile stations or service providers such as Ebay, Uber and Facebook.…”