Michael Birnhack of Tel Aviv University has written S-M-L-XL Data: Big Data as a New Informational Privacy Paradigm. Here's the abstract:
Can informational privacy law survive Big Data? A few scholars have pointed to the inadequacy of the current legal framework to Big Data, especially the collapse of notice and consent, the principles of data minimization and data specification. These are first steps, but more is needed. To better understand the informational privacy implications of Big Data, this short comment locates Big Data as the newest point on a continuum of Small-Medium-Large-Extra Large data situations. This path indicates that Big Data is not just "more of the same", but a new informational paradigm.
One suggestion is to conceptualize Big Data in terms of property: Perhaps data subjects should have a property right in their data, so that when others process it, subjects can share the wealth. However, privacy has a complex relationship with property. Lawrence Lessig's 1999 proposal to propertize personal data, was criticized: instead of more protection, said the critics, there will be more commodification. Does Big Data render property once again a viable option to save our privacy? I begin a query about the property/privacy relationship, by juxtaposing informational privacy with property, real and intangible, namely copyright. This path indicates that current property law is unfit to address Big Data.
Meanwhile, OMER TENE of the College of Management – School of Law, Israel and JULES POLONETSKY, of the Future of Privacy Forum have co-authored Judged by the Tin Man: Individual Rights in the Age of Big Data, forthcoming in the Journal of Telecommunications and High Technology Law. Here's their abstract:
Big data, the enhanced ability to collect, store and analyze previously unimaginable quantities of data in tremendous speed and with negligible costs, delivers immense benefits in marketing efficiency, healthcare, environmental protection, national security and more. While some privacy advocates may dispute the merits of sophisticated behavioral marketing practices or debate the usefulness of certain data sets to efforts to identify potential terrorists, few remain indifferent to the transformative value of big data analysis for government, science and society at large. At the same time, even big data evangelists should recognize the potentially ominous social ramifications of a surveillance society governed by heartless algorithmic machines.
In this essay, we present some of the privacy and non-privacy risks of big data as well as directions for potential solutions. In a previous paper, we argued that the central tenets of the current privacy framework, the principles of data minimization and purpose limitation, are severely strained by the big data technological and business reality. Here, we assess some of the other problems raised by pervasive big data analysis. In their book, “A Legal Theory for Autonomous Artificial Agents,” Samir Chopra and Larry White note that “as we increasingly interact with these artificial agents in unsupervised settings, with no human mediators, their seeming autonomy and increasingly sophisticated functionality and behavior, raises legal and philosophical questions.” In this article we argue that the focus on the machine is a distraction from the debate surrounding data driven ethical dilemmas, such as privacy, fairness and discrimination. The machine may exacerbate, enable, or simply draw attention to the ethical challenges, but it is humans who must be held accountable. Instead of vilifying machine-based data analysis and imposing heavy-handed regulation, which in the process will undoubtedly curtail highly beneficial activities, policymakers should seek to devise agreed-upon guidelines for ethical data analysis and profiling. Such guidelines would address the use of legal and technical mechanisms to obfuscate data; criteria for calling out unethical, if not illegal, behavior; categories of privacy and non-privacy harms; and strategies for empowering individuals through access to data in intelligible form.
