Biblio

Emerging IoT technologies promise to bring revolutionary changes to many domains including health, transportation, and building management. However, continuous monitoring of individuals threatens privacy. The success of IoT thus depends on integrating privacy protections into IoT infrastructures. This demonstration adapts a recently-proposed system, PeGaSus, which releases streaming data under the formal guarantee of differential privacy, with a state-of-the-art IoT testbed (TIPPERS) located at UC Irvine. PeGaSus protects individuals' data by introducing distortion into the output stream. While PeGaSuS has been shown to offer lower numerical error compared to competing methods, assessing the usefulness of the output is application dependent. The goal of the demonstration is to assess the usefulness of private streaming data in a real-world IoT application setting. The demo consists of a game, IoT-Detective, in which participants carry out visual data analysis tasks on private data streams, earning points when they achieve results similar to those on the true data stream. The demo will educate participants about the impact of privacy mechanisms on IoT data while at the same time generating insights into privacy-utility trade-offs in IoT applications.

Mobile and Internet-of-Things (IoT) devices, such as smartphones, tablets, wearables, smart home assistants (e.g., Google Home and Amazon Echo), and wall-mounted cameras, come equipped with various sensors, notably camera and microphone. These sensors can capture extremely sensitive and private information. There are several important scenarios where, for privacy reasons, a user might require assurance about the use (or non-use) of these sensors. For example, the owner of a home assistant might require assurance that the microphone on the device is not used during a given time of the day. Similarly, during a confidential meeting, the host needs assurance that attendees do not record any audio or video. Currently, there are no means to attain such assurance in modern mobile and IoT devices. To this end, this paper presents Ditio, a system approach for auditing sensor activities. Ditio records sensor activity logs that can be later inspected by an auditor and checked for compliance with a given policy. It is based on a hybrid security monitor architecture that leverages both ARM's virtualization hardware and TrustZone. Ditio includes an authentication protocol for establishing a logging session with a trusted server and a formally verified companion tool for log analysis. Ditio prototypes on ARM Juno development board and Nexus 5 smartphone show that it introduces negligible performance overhead for both the camera and microphone. However, it incurs up to 17% additional power consumption under heavy use for the Nexus 5 camera.

Twitter provides a public streaming API that is strictly limited, making it difficult to simultaneously achieve good coverage and relevance when monitoring tweets for a specific topic of interest. In this paper, we address the tweet acquisition challenge to enhance monitoring of tweets based on the client/application needs in an online adaptive manner such that the quality and quantity of the results improves over time. We propose a Tweet Acquisition System (TAS), that iteratively selects phrases to track based on an explore-exploit strategy. Our experimental studies show that TAS significantly improves recall of relevant tweets and the performance improves when the topics are more specific.