Orca: FSS-based Secure Training with GPUs

Secure Two-party Computation (2PC) allows two parties to compute any function on their private inputs without revealing their inputs in the clear to each other. Since 2PC is known to have notoriously high overheads, one of the most popular computation models is that of 2PC with a trusted dealer, where a trusted dealer provides correlated randomness (independent of any input) to both parties during a preprocessing phase. Recent works construct efficient 2PC protocols in this model based on the cryptographic technique of function secret sharing (FSS). We build an end-to-end system Orca to accelerate the computation of FSS-based 2PC protocols with GPUs. Next, we observe that the main performance bottleneck in such accelerated protocols is in storage (due to the large amount of correlated randomness), and we design new FSS-based 2PC cryptographic protocols for several key functionalities in ML which reduce storage by up to $5\times$. Compared to prior state-of-the-art on secure training accelerated with GPUs in the same computation model (Piranha, Usenix Security 2022), we show that Orca has $4\%$ higher accuracy, $123\times$ lesser communication, and is $19\times$ faster on CIFAR-10

SIGMA: Secure GPT Inference with Function Secret Sharing

Secure 2-party computation (2PC) enables secure inference that offers protection for both proprietary machine learning (ML) models and sensitive inputs to them. However, the existing secure inference solutions suffer from high latency and communication overheads, particularly for transformers. Function secret sharing (FSS) is a recent paradigm for obtaining efficient 2PC protocols with a preprocessing phase. We provide SIGMA, the first end-to-end system for secure transformer inference based on FSS. By constructing new FSS-based protocols for complex machine learning functionalities, such as Softmax and GeLU, and also accelerating their computation on GPUs, SIGMA improves the latency of secure inference of transformers by $11-19\times$ over the state-of-the-art that uses preprocessing and GPUs. We present the first secure inference of generative pre-trained transformer (GPT) models. In particular, SIGMA executes GPT-Neo with 1.3 billion parameters in 7.4s and HuggingFace\u27s GPT2 in 1.6s

Optimal investment and consumption strategy for a retiree under stochastic force of mortality

With an increase in the self-driven retirement plans during past few decades, more and more retirees are managing their retirement portfolio on their own. Therefore, they need to know the optimal amount of consumption they can afford each year, and the optimal proportion of wealth they should invest in the financial market. In this project, we study the optimization strategy proposed by Delong and Chen (2016). Their model determines the optimal consumption and investment strategy for a retiree facing (1) a minimum lifetime consumption, (2) a stochastic force of mortality following a geometric Brownian motion process, (3) an annuity income, and (4) non-exponential discounting of future income. We use a modified version of the Cox, Ingersoll, and Ross (1985) model to capture the stochastic mortality intensity of the retiree and, subsequently, determine a new optimal consumption and investment strategy using their framework. We use an expansion method to solve the classic Hamilton-Jacobi-Bellman equation by perturbing the non-exponential discounting parameter using partial differential equations

Sciatic nerve stripping: A rare avulsion injury

Isolated injury to sciatic nerve is not a common entity because of its deeper location and is often associated with musculoskeletal injuries. Though injuries caused by penetratingtrauma,injections and hip surgery are well reported,a self- inflicted complete avulsion of this thickest nerve of the body is unreported. We report a peculiar case of avulsion injury of the sciatic nerve both in terms of its mechanism and the extent of injury. The sciatic nerve along with its branches (tibial and common peroneal nerve) was completely avulsed off its entire anatomical course by a penetrating injury at the buttock. The resulting nerve gaps were reconstructed with sural and superficial peroneal nerve grafts harvested from both lower limbs. Prognostically, proximal nerve injuries in lower extremity fare worse than the upper extremity in terms of recovery after peripheral nerve repair or reconstruction