Given the size of the Big Data and IoT (Internet of Things) it is clear that we really have no privacy, just consider Justice Sonia Sotomayor’s opinion that “GPS monitoring generates a precise, comprehensive record of a person’s public movements that reflects a wealth of detail about her familial, political, professional, religious, and sexual associations.” Justice Sotomayor’s opinion was in the 2012 US v. Jones case in which the US Supreme Court ruled 9-0 that the 4 weeks of GPS data about an alleged drug dealer’s location obtained from a GPS device attached to his car without a warrant, violated the defendant’s Fourth Amendment guarantee of privacy.
A recent issue of the New York University Journal of Law and Liberty included an article about the privacy under the Fourth Amendment entitled “When Enough Is Enough: Location Tracking, Mosaic Theory, and Machine Learning.” The NYU article was written by Steven M. Bellovin (Professor, Columbia University, Department of Computer Science), Renée M. Hutchins (Associate Professor, University of Maryland Francis King Carey School of Law), Tony Jebara (Associate Professor, Columbia University, Department of Computer Science), and Sebastian Zimmeck (Ph.D. candidate, Columbia University, Department of Computer Science).
The NYU article included a description of “Unsupervised Machine Learning” which “automatically finds dependencies, correlations, and clusters in the data without requiring any significant human intervention. More specifically, it could perform the following operations”:
Of course the article includes a discussion of Supervised Machine Learning which is “more laborious to create since it requires human an-notation effort while unsupervised learning is more of a pure data collection exercise. With supervised learning, we can perform the following operations with varying degrees of accuracy”:
Even with the FTC’s recent call to Congress to control Big Data it seems likely that we have less privacy given the size and scope data analysis of Big Data and IoT.