Fast Actively Secure Five-Party Computation with Security Beyond Abort

Secure Multi-party Computation (MPC) with small population and honest majority has drawn focus specifically due to customization in techniques and resulting efficiency that the constructions can offer. In this work, we investigate a wide range of security notions in the five-party setting, tolerating two active corruptions. Being constant-round, our protocols are best suited for real-time, high latency networks such as the Internet. In a minimal setting of pairwise-private channels, we present efficient instantiations with unanimous abort (where either all honest parties obtain the output or none of them do) and fairness (where the adversary obtains its output only if all honest parties also receive it). With the presence of an additional broadcast channel (known to be necessary), we present a construction with guaranteed output delivery (where any adversarial behaviour cannot prevent the honest parties from receiving the output). The broadcast communication is minimal and independent of circuit size. In terms of performance (communication and run time), our protocols incur minimal overhead over the best known protocol of Chandran et al. (ACM CCS 2016) that achieves the least security notion of selective abort. Further, our protocols for fairness and unanimous abort can be extended to n-parties with at most $\sqrt{n}$ corruptions, similar to Chandran et al. Going beyond the most popular honest-majority setting of three parties with one corruption, our results demonstrate feasibility of attaining stronger security notions at an expense not too far from the least desired security of selective abort

SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning

Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recently, PPML has seen a visible shift towards the adoption of the Secure Outsourced Computation~(SOC) paradigm due to the heavy computation that it entails. In the SOC paradigm, computation is outsourced to a set of powerful and specially equipped servers that provide service on a pay-per-use basis. In this work, we propose SWIFT, a robust PPML framework for a range of ML algorithms in SOC setting, that guarantees output delivery to the users irrespective of any adversarial behaviour. Robustness, a highly desirable feature, evokes user participation without the fear of denial of service. At the heart of our framework lies a highly-efficient, maliciously-secure, three-party computation (3PC) over rings that provides guaranteed output delivery (GOD) in the honest-majority setting. To the best of our knowledge, SWIFT is the first robust and efficient PPML framework in the 3PC setting. SWIFT is as fast as (and is strictly better in some cases than) the best-known 3PC framework BLAZE (Patra et al. NDSS'20), which only achieves fairness. We extend our 3PC framework for four parties (4PC). In this regime, SWIFT is as fast as the best known fair 4PC framework Trident (Chaudhari et al. NDSS'20) and twice faster than the best-known robust 4PC framework FLASH (Byali et al. PETS'20). We demonstrate our framework's practical relevance by benchmarking popular ML algorithms such as Logistic Regression and deep Neural Networks such as VGG16 and LeNet, both over a 64-bit ring in a WAN setting. For deep NN, our results testify to our claims that we provide improved security guarantee while incurring no additional overhead for 3PC and obtaining 2x improvement for 4PC.Comment: This article is the full and extended version of an article to appear in USENIX Security 202