Big privacy: challenges and opportunities of privacy study in the age of big data

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 first present an overview of the battle ground by defining the roles and operations of privacy systems. Second, we review the milestones of the current two major research categories of privacy: data clustering and privacy frameworks. Third, we discuss the effort of privacy study from the perspectives of different disciplines, respectively. Fourth, the mathematical description, measurement, and modeling on privacy are presented. We summarize the challenges and opportunities of this promising topic at the end of this paper, hoping to shed light on the exciting and almost uncharted land

Data Analytics in Higher Education: Key Concerns and Open Questions

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas

Familiarity with Big Data, Privacy Concerns, and Self-disclosure Accuracy in Social Networking Websites: An APCO Model

Social networking websites have not only become the most prevalent communication tools in today’s digital age but also one of the top big data sources. Big data advocates promote the promising benefits of big data applications to both users and practitioners. However, public polls show evidence of heightened privacy concerns among Internet and social media users. We review the privacy literature based on protection motivation theory and the theory of planned behavior to develop an APCO model that incorporates novel factors that reflect users’ familiarity with big data. Our results, which we obtained from using a cross-sectional survey design and structural equation modeling (SEM) techniques, support most of our proposed hypotheses. Specifically, we found that that awareness of big data had a negative impact on and awareness of big data implications had a positive impact on privacy concerns. In turn, privacy concerns impacted self-disclosure concerns positively and self-disclosure accuracy negatively. We also considered other antecedents of privacy concerns and tested other alternative models to examine the mediating role of privacy concerns, to control for demographic variables, and to investigate different roles of the trust construct. Finally, we discuss the results of our findings and the theoretical and practical implications

The Role of Privacy-Preserving Technologies in the Age of Big Data

The potential social and economic benefits of big data applications are highlighted by researchers and the media alike. However, they can also have negative implications, which are not limited to privacy issues. With alarming regularity, massive data breaches become public. Measures taken by both policy makers and business leaders do not seem to be effective. Privacy preserving technologies have long been a hot topic in research, but they have not yet been widely integrated into big data solutions. To understand the mechanisms that drive or prevent the deployment of privacy-preserving technologies better, we investigated their effectiveness and the challenges they pose as well as their perception and use in the context of big data. The findings indicate that privacy-preserving technologies are quite mature, have different aims and need to be combined to be effective. The mechanisms that affect their deployment are manifold

FROM DATA PROTECTION TO \uabPRIVACY BY RESEARCH\ubb FOOD FOR THOUGHT IN THE LIGHT OF THE NEW EUROPEAN GENERAL DATA PROTECTION REGULATION

The paper recalls the process which led to the adoption of the new european Data Protection Regulation, in the context of the rapid development of the Information and Communication Technologies and the amazing increase of data flows (Big data). Data Protection and Privacy Protection could be seen as limits to the development of technologies. On the other hand, the rapid evolution of Smart cities and ICTs brings new risks for the protection of fundamental rights. The new european Regulation n. 2016/679 could be insufficient to protect privacy rights in the age of Big data. Maybe some new instrument is necessary to protect personal data and, consequently, privacy. The paper proposes the concept of \uabPrivacy by Research\ubb, intended as a new privacy-friendly method of design for devices, databases and apps

Digitizing The Fourth Amendment: Privacy in the Age of Big Data Policing

Today’s availability of massive data sets, inexpensive data storage, and sophisticated analytical software has transformed the capabilities of law enforcement and created new forms of “Big Data Policing.” While Big Data Policing may improve the administration of public safety, these methods endanger constitutional protections against warrantless searches and seizures. This Article explores the Fourth Amendment consequences of Big Data Policing in three parts. First, it provides an overview of Fourth Amendment jurisprudence and its evolution in light of new policing technologies. Next, the Article reviews the concept of “Big Data” and examines three forms of Big Data Policing: Predictive Policing Technology (PPT); data collected by third-parties and purchased by law enforcement; and geofence warrants. Finally, the Article concludes with proposed solutions to rebalance the protections afforded by the Fourth Amendment against these new forms of policing

Notice and Consent in a World of Big Data

Nowadays individuals are often presented with long and complex privacy notices routinely written by lawyers for lawyers, and are then requested to either ‘consent’ or abandon the use of the desired service. The over-use of notice and consent presents increasing challenges in an age of ‘Big Data’. These phenomena are receiving attention particularly in the context of the current review of the OECD Privacy Guidelines. In 2012 Microsoft sponsored an initiative designed to engage leading regulators, industry executives, public interest advocates, and academic experts in frank discussions about the role of individual control and notice and consent in data protection today, and alternative models for providing better protection for both information privacy and valuable data flows in the emerging world of Big Data and cloud computing

When private set intersection meets big data : an efficient and scalable protocol

Large scale data processing brings new challenges to the design of privacy-preserving protocols: how to meet the increasing requirements of speed and throughput of modern applications, and how to scale up smoothly when data being protected is big. Efficiency and scalability become critical criteria for privacy preserving protocols in the age of Big Data. In this paper, we present a new Private Set Intersection (PSI) protocol that is extremely efficient and highly scalable compared with existing protocols. The protocol is based on a novel approach that we call oblivious Bloom intersection. It has linear complexity and relies mostly on efficient symmetric key operations. It has high scalability due to the fact that most operations can be parallelized easily. The protocol has two versions: a basic protocol and an enhanced protocol, the security of the two variants is analyzed and proved in the semi-honest model and the malicious model respectively. A prototype of the basic protocol has been built. We report the result of performance evaluation and compare it against the two previously fastest PSI protocols. Our protocol is orders of magnitude faster than these two protocols. To compute the intersection of two million-element sets, our protocol needs only 41 seconds (80-bit security) and 339 seconds (256-bit security) on moderate hardware in parallel mode

Big Data for All: Privacy and User Control in the Age of Analytics

We live in an age of “big data.” Data have become the raw material of production, a new source for immense economic and social value. Advances in data mining and analytics and the massive increase in computing power and data storage capacity have expanded by orders of magnitude the scope of information available for businesses and government. Data are now available for analysis in raw form, escaping the confines of structured databases and enhancing researchers’ abilities to identify correlations and conceive of new, unanticipated uses for existing information. In addition, the increasing number of people, devices, and sensors that are now connected by digital networks has revolutionized the ability to generate, communicate, share, and access data. Data creates enormous value for the world economy, driving innovation, productivity, efficiency, and growth. At the same time, the “data deluge” presents privacy concerns which could stir a regulatory backlash dampening the data economy and stifling innovation. In order to craft a balance between beneficial uses of data and individual privacy, policymakers must address some of the most fundamental concepts of privacy law, including the definition of “personally identifiable information,” the role of individual control, and the principles of data minimization and purpose limitation. This article emphasizes the importance of providing individuals with access to their data in usable format. This will let individuals share the wealth created by their information and incentivize developers to offer user-side features and applications harnessing the value of big data. Where individual access to data is impracticable, data are likely to be de-identified to an extent sufficient to diminish privacy concerns. In addition, since in a big data world it is often not the data but rather the inferences drawn from them that give cause for concern, organizations should be required to disclose their decisional criteria