Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data.

Behavioral data, collected from our daily interactions with technology, have driven scientific advances. Yet, the collection and sharing of this data raise legitimate privacy concerns, as individuals can often be reidentified. Current identification attacks, however, require auxiliary information to roughly match the information available in the dataset, limiting their applicability. We here propose an entropy-based profiling model to learn time-persistent profiles. Using auxiliary information about a single target collected over a nonoverlapping time period, we show that individuals are correctly identified 79% of the time in a large location dataset of 0.5 million individuals and 65.2% for a grocery shopping dataset of 85,000 individuals. We further show that accuracy only slowly decreases over time and that the model is robust to state-of-the-art noise addition. Our results show that much more auxiliary information than previously believed can be used to identify individuals, challenging deidentification practices and what currently constitutes legally anonymous data.

1T Pixel Using Floating-Body MOSFET for CMOS Image Sensors.

We present a single-transistor pixel for CMOS image sensors (CIS). It is a floating-body MOSFET structure, which is used as photo-sensing device and source-follower transistor, and can be controlled to store and evacuate charges. Our investigation into this 1T pixel structure includes modeling to obtain analytical description of conversion gain. Model validation has been done by comparing theoretical predictions and experimental results. On the other hand, the 1T pixel structure has been implemented in different configurations, including rectangular-gate and ring-gate designs, and variations of oxidation parameters for the fabrication process. The pixel characteristics are presented and discussed.