HEQuant: Marrying Homomorphic Encryption and Quantization for Communication-Efficient Private Inference

Secure two-party computation with homomorphic encryption (HE) protects data privacy with a formal security guarantee but suffers from high communication overhead. While previous works, e.g., Cheetah, Iron, etc, have proposed efficient HE-based protocols for different neural network (NN) operations, they still assume high precision, e.g., fixed point 37 bit, for the NN operations and ignore NNs' native robustness against quantization error. In this paper, we propose HEQuant, which features low-precision-quantization-aware optimization for the HE-based protocols. We observe the benefit of a naive combination of quantization and HE quickly saturates as bit precision goes down. Hence, to further improve communication efficiency, we propose a series of optimizations, including an intra-coefficient packing algorithm and a quantization-aware tiling algorithm, to simultaneously reduce the number and precision of the transferred data. Compared with prior-art HE-based protocols, e.g., CrypTFlow2, Cheetah, Iron, etc, HEQuant achieves $3.5\sim 23.4\times$ communication reduction and $3.0\sim 9.3\times$ latency reduction. Meanwhile, when compared with prior-art network optimization frameworks, e.g., SENet, SNL, etc, HEQuant also achieves $3.1\sim 3.6\times$ communication reduction

The Impact of Reviews of Physicians on Patient Choice

How reviews impact patient choice in health field is still unknown. Patients often worry about their diseases and are eager to find a high skill physician to cure their painful. Traditional hospitals often lack information about individual physician, and with the emergence of online health communities (OHCs), patients can get physician service information on the platform. This study researches the role of reviews in health field and how the roles change with different diseases by collecting data from an online health community. We divide patient reviews into two kinds: online service reviews and offline service reviews based on different services. We find disease risk significantly moderates the relationship between reviews and patient choice: when patients get high-risk diseases, they care more offline service reviews than low-risk diseases. On the contrary, when patients get low-risk diseases, they care more online service reviews than high-risk diseases

Shaping the Emerging Norms of Using Large Language Models in Social Computing Research

The emergence of Large Language Models (LLMs) has brought both excitement and concerns to social computing research. On the one hand, LLMs offer unprecedented capabilities in analyzing vast amounts of textual data and generating human-like responses, enabling researchers to delve into complex social phenomena. On the other hand, concerns are emerging regarding the validity, privacy, and ethics of the research when LLMs are involved. This SIG aims at offering an open space for social computing researchers who are interested in understanding the impacts of LLMs to discuss their current practices, perspectives, challenges when engaging with LLMs in their everyday work and collectively shaping the emerging norms of using LLMs in social computing research