As Internet-of-Things (IoT) devices rapidly gain popularity, they raise significant privacy concerns given the breadth of sensitive data they can capture. These concerns are amplified by the fact that in many situations, IoT devices collect data about people other than their owner or administrator, and these stakeholders have no say in how that data is managed, used, or shared. To address this, we propose a new model of ownership, IoT Ephemeral Ownership (TEO). TEO allows stakeholders to quickly register with an IoT device for a limited period, and thus claim co-ownership over the sensitive data that the device generates. Device admins retain the ability to decide who may become an ephemeral owner, but no longer have access or control to the private data generated by the device. The encrypted data in TEO is accessible only by entities after seeking explicit permission from the different co-owners of that data. We verify the key security properties of our protocol underpinning TEO in the symbolic model using ProVerif. We also implement a cross-platform prototype of TEO for mobile phones and embedded devices, and integrate it into three real-world application case studies. Our evaluation shows that the latency and battery impact of TEO is typically small, adding ≤ 187 ms onto one-time operations, and introducing limited (<25%) overhead on recurring operations like private data storage.

This demonstration presents a working prototype of TEO, a new model of device ownership that divides traditional owners into "admin" and "ephemeral owners". TEO addresses the challenge that users have no say in how their data are managed when the device is controlled by third parties, such as in rental and shared spaces. We design a complete protocol suite to address several practical issues, including preserving data ownership after users stop using the device, minimizing trusts with untrusted storage providers, enabling access control and revocation, and supporting groups of owners. Our cross-platform prototype implementation enables us to demonstrate the operational flow for managing ephemeral ownership of TEO-enabled devices in a representative setup with mobile phones, embedded devices, and Linux servers.

In this mixed-methods study of political discourse, we study the affordances of Twitter in the context of free speech in India. We critically examine specific cases of the legal prosecution of free speech and the use of extreme speech in attacks on people to document the risks to citizens when they engage in antagonistic online discourse, particularly against the state or political institutions. We follow this up with quantitative study of the use of extreme speech through 477 hashtags used by a random sample of thousand political actors on Twitter and find that politicians are rewarded, through higher retweet rates, when they engage in extreme or uncivil messaging. We contextualize these findings to the postcolonial history of India and the laws and institutions that enable differential consequences for engaging in various forms of speech. In conclusion, we propose that the affordances of new ICTs like social media need to be carefully considered for their unintended consequences, and that functional access to free speech may differ dramatically based on one's access to institutions.

Despite the popularity of recommendation services (such as Yelp, Healthgrades, and Angie’s List), for a majority of entities listed on these services, one has to rely on opinions shared by a few users. We argue that this paucity of reviews for most entities stems from the fact that the vast majority of users largely consume opinions shared by others but seldom post reviews themselves. Therefore, leveraging the trend that services are increasingly accessed from a client-side app rather than over the Web, we propose augmenting recommendation services to implicitly infer any user’s opinions based on observations of the user’s activities. Implicit inference of many of a user’s recommendations are feasible due to the rich sensory capabilities of smartphones and wearables as well as the digital footprints left behind by many activities in the physical world. However, implicit inference of opinions is inherently uncertain and automated sharing of inferences raises significant privacy and security concerns. In this paper, we discuss how to tackle these challenges so that users looking for recommendations can draw upon a more comprehensive set of opinions than is the case today.