Measuring and Protecting Privacy in the Always-On Era

Data-mining practices have greatly advanced in the interconnected era. What began with the internet now continues through the Internet of Things (IoT)--whereby users can constantly be connected to the internet through various means, like televisions, smartphones, wearables, and computerized personal assistants, among other "things." As many of these devices constantly receive and transmit data, the increased use of IoT devices might lead society into an "always-on" era, where individuals are "datafied"--constantly quantified and tracked. This situation leads to difficult policy choices. On the one hand, the current sectorial regulatory approach, which protects privacy through regulating information gathering or use only in predefined industries or specified cohorts, greatly risks individuals' privacy. On the other hand, strict privacy regulations might diminish data utility, which is crucial for technological development and innovation. There is a tradeoff between data utility and privacy protection, and the sectoral approach to privacy does not strike the right balance. This Article proposes a technological solution that might help. Relying on a method called differential privacy, this Article suggests adding "noise" to data deemed sensitive ex ante. In short, combining computational solutions with formulas that measure the probability of data sensitivity will better protect privacy in the always-on era. This Article introduces legal and computational methods that could be used by IoT service providers and can optimally balance the tradeoff between data utility and privacy. Part II discusses the protection of privacy under the sectoral approach and estimates what values this approach embeds. Part III discusses privacy protection in the "always-on" era. This Part assesses how technological changes have shaped the sectoral regulation regime, then discusses why IoT devices negatively impact privacy, and finally explores the potential regulatory mechanisms that might meet the challenges of the "always-on" era. After concluding that the current regulatory framework is severely limited in protecting individuals' privacy, this Article discusses technology as a solution in Part IV. This Part proposes a new computational model that relies on differential privacy and a modern invention called private coresets. This proposed model introduces "noise" to users' data according to the probability that the IoT device collects sensitive data, in order to preserve individuals' privacy and ensure service providers can utilize the data at the same time.