Lookup table (LUT) is an important cryptography primitive, widely used in secure applications such as private set intersection, boolean circuit evaluation, and privacy-preserving machine learning. However, existing LUT constructions suffer from either high overhead or limited functionality. In this paper, we propose ROTL, a secure two-party protocol for arithmetic LUT evaluation. Compared with SP-LUT (the state-of-the-art arithmetic LUT presented at NDSS '17), it achieves up to 3.3x speedup and 10.5x communication reduction in overall (preprocessing + online) and 21x speedup and 60x communication reduction in terms of the online phase. At the heart of ROTL is a novel protocol for secret-sharing rotation, which allows two parties to generate additive secret shares of the rotated table without revealing the rotation offset. We believe this protocol is of independent interest. Based on ROTL, we design a novel secure comparison protocol; compared with the state-of-the-art (USENIX '22), it achieves a 5x runtime speedup and 2.5x communication reduction in the online performance. To support boolean secret sharing, we further provide an optimization (named FLUTE+) for FLUTE (the state-of-the-art boolean LUT presented at Oakland '23). For a boolean LUT with table size n and elements bit-width l, we reduce FLUTE's computation complexity from O(n^2 l) to O(n log n + n l) and shift O(n log n) computation to the preprocessing phase without introducing communication overhead. As a result, FLUTE+ achieves up to 5x speedup in terms of overall (preprocessing and online) and over 600x speedup in terms of the online phase compared with FLUTE. The communication cost of FLUTE+ is exactly the same as FLUTE's in both the preprocessing phase and the online phase.

Secure two-party machine learning has made substantial progress through the use of mixed-mode protocols, but existing approaches often suffer from efficiency bottlenecks due to inherent mismatch between optimal domains of various cryptographic primitives. In response to these challenges, we introduce framework M&M, which features an efficient modulus conversion protocol. This breakthrough enables seamless integration of the most suitable cryptographic subprotocols within their optimal modulus domains with a minimal modulus conversion overhead. We further establish new benchmarks and practical optimizations for the performance of fundamental primitives, namely comparison and multiplication, across various two-party techniques.By incorporating these techniques, M&M demonstrates significant performance enhancements over state-of-the-art solutions: i) we report a$6\times$-$100\times$improvement for approximated truncations with 1-bit error tolerance; ii) an average of$5\times$(resp.$4\times$) reduction in communication (resp. runtime) for machine learning functions; iii) and a 25%-99% improvement in cost-efficiency for private inference of deep neural networks and 50% improvement in private training of gradient boosting decision trees.

ChatGPT is recognized as a significant revolution in the field of artificial intelligence, but it raises serious concerns regarding user privacy, as the data submitted by users may contain sensitive information. Existing solutions for secure inference face significant challenges in supporting GPT-like models due to the enormous number of model parameters and complex activation functions. In this paper, we develop CipherGPT, the first framework for secure two-party GPT inference, building upon a series of innovative protocols. First, we propose a secure matrix multiplication that is customized for GPT inference, achieving upto 3.8× speedup and 4.3× bandwidth reduction over SOTA. We also propose a novel protocol for securely computing GELU, surpassing SOTA by 3.2× in runtime, 1.3× in communication and 7.4× in precision. Furthermore, we propose the first protocol for secure top-k sampling. We provide a full-fledged implementation and comprehensive benchmark for CipherGPT. In particular, we measure the runtime and communication for each individual operation. We believe this will serve as a reference for future research in this area.

Graph clustering is an unsupervised machine learning method that partitions the nodes in a graph into different groups. Despite achieving significant progress in exploiting both attributed and structured data information, graph clustering methods often face practical challenges related to data isolation. Moreover, the absence of collaborative methods for graph clustering limits their effectiveness. In this paper, we propose a collaborative graph clustering framework for attributed graphs, supporting attributed graph clustering over vertically partitioned data with different participants holding distinct features of the same data. Our method leverages a novel technique that reduces the sample space, improving the efficiency of the attributed graph clustering method. Furthermore, we compare our method to its centralized counterpart under a proximity condition, demonstrating that the successful local results of each participant contribute to the overall success of the collaboration. We fully implement our approach and evaluate its utility and efficiency by conducting experiments on four public datasets. The results demonstrate that our method achieves comparable accuracy levels to centralized attributed graph clustering methods. Our collaborative graph clustering framework provides an efficient and effective solution for graph clustering challenges related to data isolation.

Encrypted packet inspection (EPI) allows a middle-box to perform DPI over encrypted packets without decryption. Existing EPI systems rely on expensive cryptographic operations, hence they are not yet ready to be deployed in real-world. Fur-thermore, such solutions only support exact keyword matching, unable to securely support regular expression, which is the major tool for DPI rule description due to its powerful and flexible expressive ability. In this paper, we propose PrivRE, the first EPI system that can securely support regular expressions. The main idea of PrivRE is to have middlebox run regular expressions on a desensitized version of the payload, in which sensitive information has been replaced with dummy characters. We provide a full-fledged implementation of PrivRE. In particular, we override OpenSSL to make PrivRE transparent to the application layer, so that the software developers do not need to be aware of the existence of PrivRE. We systematically evaluate PrivRE on a testbed that consists of 3 intercontinental EC2 VMs. Our experimental results show that it introduces at most 0.03 % accuracy loss, and it is only 1.78 x −8.23 x slower than SplitTLS (where the middle box can decrypt the packets).

Federated Learning (FL) has been an emerging trend in machine learning and artificial intelligence. It allows multiple participants to collaboratively train a better global model and offers a privacy-aware paradigm for model training since it does not require participants to release their original training data. However, existing FL solutions for vertically partitioned data or decision trees require heavy cryptographic operations. In this article, we propose a framework named$\mathsf {FederBoost}$for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both vertically and horizontally partitioned data. Vertical$\mathsf {FederBoost}$doesnotrequire any cryptographic operation and horizontal$\mathsf {FederBoost}$only requires lightweight secure aggregation. The key observation is that the whole training process of GBDT relies on theorderingof the data instead of the values. We fully implement$\mathsf {FederBoost}$and evaluate its utility and efficiency through extensive experiments performed on three public datasets. Our experimental results show that both vertical and horizontal$\mathsf {FederBoost}$achieve the same level of accuracy with centralized training where all data are collected in a central server; and they are 4-5 orders of magnitude faster than the state-of-the-art solutions for federated decision tree training; hence offering practical solutions for industrial applications.

MOTIVATION: N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites. RESULTS: In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.