HABIT: Hardware-Assisted Bluetooth-based Infection Tracking

The ongoing COVID-19 pandemic has caused health organizations to consider using digital contact tracing to help monitor and contain the spread of COVID-19. Due to this urgent need, many different groups have developed secure and private contact tracing phone apps. However, these apps have not been widely deployed, in part because they do not meet the needs of healthcare officials. We present HABIT, a contact tracing system using a wearable hardware device designed specifically with the goals of public health officials in mind. Unlike current approaches, we use a dedicated hardware device instead of a phone app for proximity detection. Our use of a hardware device allows us to substantially improve the accuracy of proximity detection, achieve strong security and privacy guarantees that cannot be compromised by remote attackers, and have a more usable system, while only making our system minimally harder to deploy compared to a phone app in centralized organizations such as hospitals, universities, and companies. The efficacy of our system is currently being evaluated in a pilot study at Yale University in collaboration with the Yale School of Public Health

Data-Induced Interactions of Sparse Sensors

Large-dimensional empirical data in science and engineering frequently has low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system. The quality of this reconstruction, especially in the presence of sensor noise, depends significantly on the spatial configuration of the sensors. Multiple algorithms based on gappy interpolation and QR factorization have been proposed to optimize sensor placement. Here, instead of an algorithm that outputs a singular "optimal" sensor configuration, we take a thermodynamic view to compute the full landscape of sensor interactions induced by the training data. The landscape takes the form of the Ising model in statistical physics, and accounts for both the data variance captured at each sensor location and the crosstalk between sensors. Mapping out these data-induced sensor interactions allows combining them with external selection criteria and anticipating sensor replacement impacts.Comment: 17 RevTeX pages, 10 figure

Self-Processing Private Sensor Data via Garbled Encryption

We introduce garbled encryption, a relaxation of secret-key multi-input functional encryption (MiFE) where a function key can be used to jointly compute upon only a particular subset of all possible tuples of ciphertexts. We construct garbled encryption for general functionalities based on one-way functions. We show that garbled encryption can be used to build a self-processing private sensor data system where after a one-time trusted setup phase, sensors deployed in the field can periodically broadcast encrypted readings of private data that can be computed upon by anyone holding function keys to learn processed output, without any interaction. Such a system can be used to periodically check, e.g., whether a cluster of servers are in an alarm state. We implement our garbled encryption scheme and find that it performs quite well, with function evaluations in the microseconds. The performance of our scheme was tested on a standard commodity laptop