LLAMA: A Low Latency Math Library for Secure Inference

Secure machine learning (ML) inference can provide meaningful privacy guarantees to both the client (holding sensitive input) and the server (holding sensitive weights of the ML model) while realizing inference-as-a-service. Although many specialized protocols exist for this task, including those in the preprocessing model (where a majority of the overheads are moved to an input independent offline phase), they all still suffer from large online complexity. Specifically, the protocol phase that executes once the parties know their inputs, has high communication, round complexity, and latency. Function Secret Sharing (FSS) based techniques offer an attractive solution to this in the trusted dealer model (where a dealer provides input independent correlated randomness to both parties), and 2PC protocols obtained based on these techniques have a very lightweight online phase. Unfortunately, current FSS-based 2PC works (AriaNN, PoPETS 2022; Boyle et al. Eurocrypt 2021; Boyle et al. TCC 2019) fall short of providing a complete solution to secure inference. First, they lack support for math functions (e.g., sigmoid, and reciprocal square root) and hence, are insufficient for a large class of inference algorithms (e.g. recurrent neural networks). Second, they restrict all values in the computation to be of the same bitwidth and this prevents them from benefitting from efficient float-to-fixed converters such as Tensorflow Lite that crucially use low bitwidth representations and mixed bitwidth arithmetic. In this work, we present LLAMA -- an end-to-end, FSS based, secure inference library supporting precise low bitwidth computations (required by converters) as well as provably precise math functions; thus, overcoming all the drawbacks listed above. We perform an extensive evaluation of LLAMA and show that when compared with non-FSS based libraries supporting mixed bitwidth arithmetic and math functions (SIRNN, IEEE S&P 2021), it has at least an order of magnitude lower communication, rounds, and runtimes. We integrate LLAMA with the EzPC framework (IEEE EuroS&P 2019) and demonstrate its robustness by evaluating it on large benchmarks (such as ResNet-50 on the ImageNet dataset) as well as on benchmarks considered in AriaNN -- here too LLAMA outperforms prior work

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

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