Aegis: A Lightning Fast Privacy-preserving Machine Learning Platform against Malicious Adversaries

Privacy-preserving machine learning (PPML) techniques have gained significant popularity in the past years. Those protocols have been widely adopted in many real-world security-sensitive machine learning scenarios, e.g., medical care and finance. In this work, we introduce $\mathsf{Aegis}$~-- a high-performance PPML platform built on top of a maliciously secure 3-PC framework over ring $\mathbb{Z}_{2^\ell}$. In particular, we propose a novel 2-round secure comparison (a.k.a., sign bit extraction) protocol in the preprocessing model. The communication of its semi-honest version is only 25% of the state-of-the-art (SOTA) constant-round semi-honest comparison protocol by Zhou et al.(S&P 2023); both communication and round complexity of its malicious version are approximately 50% of the SOTA (BLAZE) by Patra and Suresh (NDSS 2020), for $\ell=64$. Moreover, the communication of our maliciously secure inner product protocol is merely $3\ell$ bits, reducing 50% from the SOTA (Swift) by Koti et al. (USENIX 2021). Finally, the resulting ReLU and MaxPool PPML protocols outperform the SOTA by $4\times$ in the semi-honest setting and $10\times$ in the malicious setting, respectively

UC Secure Private Branching Program and Decision Tree Evaluation

Branching program (BP) is a DAG-based non-uniform computational model for L/poly class. It has been widely used in formal verification, logic synthesis, and data analysis. As a special BP, a decision tree is a popular machine learning classifier for its effectiveness and simplicity. In this work, we propose a UC-secure efficient 3-party computation platform for outsourced branching program and/or decision tree evaluation. We construct a constant-round protocol and a linear-round protocol. In particular, the overall (online + offline) communication cost of our linear-round protocol is $O(d(\ell + \log m+\log n))$ and its round complexity is $2d-1$, where $m$ is the DAG size, $n$ is the number of features, $\ell$ is the feature length, and $d$ is the longest path length. To enable efficient oblivious hopping among the DAG nodes, we propose a lightweight $1$-out-of-$N$ shared OT protocol with logarithmic communication in both online and offline phase. This partial result may be of independent interest to some other cryptographic protocols. Our benchmark shows, compared with the state-of-the-arts, the proposed constant-round protocol is up to 10X faster in the WAN setting, while the proposed linear-round protocol is up to 15X faster in the LAN setting

ABCC3 as a marker for multidrug resistance in non-small cell lung cancer

Multidrug resistance (MDR) contributes to the failure of chemotherapy and high mortality in non-small cell lung cancer (NSCLC). We aim to identify MDR genes that predict tumor response to chemotherapy. 199 NSCLC fresh tissue samples were tested for chemosensitivity by MTT assay. cDNA microarray was done with 5 samples with highest resistance and 6 samples with highest sensitivity. Expression of ABCC3 mRNA and protein was detected by real-time PCR and immunohistochemisty, respectively. The association between gene expression and overall survival (OS) was examined using Cox proportional hazard regression. 44 genes were upregulated and 168 downregulated in the chemotherapy-resistant group. ABCC3 was one of the most up-regulated genes in the resistant group. ABCC3-positive expression correlated with lymph node involvement, advanced TNM stage, more malignant histological type, multiple-resistance to anti-cancer drugs, and reduced OS. ABCC3 expression may serve as a marker for MDR and predictor for poor clinical outcome of NSCLC

Potential therapeutic application of gold nanoparticles in B-chronic lymphocytic leukemia (BCLL): enhancing apoptosis

B-Chronic Lymphocytic Leukemia (CLL) is an incurable disease predominantly characterized by apoptosis resistance. We have previously described a VEGF signaling pathway that generates apoptosis resistance in CLL B cells. We found induction of significantly more apoptosis in CLL B cells by co-culture with an anti-VEGF antibody. To increase the efficacy of these agents in CLL therapy we have focused on the use of gold nanoparticles (GNP). Gold nanoparticles were chosen based on their biocompatibility, very high surface area, ease of characterization and surface functionalization. We attached VEGF antibody (AbVF) to the gold nanoparticles and determined their ability to kill CLL B cells. Gold nanoparticles and their nanoconjugates were characterized using UV-Visible spectroscopy (UV-Vis), transmission electron microscopy (TEM), thermogravimetric analysis (TGA) and X-ray photoelectron spectroscopy (XPS). All the patient samples studied (N = 7) responded to the gold-AbVF treatment with a dose dependent apoptosis of CLL B cells. The induction of apoptosis with gold-AbVF was significantly higher than the CLL cells exposed to only AbVF or GNP. The gold-AbVF treated cells showed significant down regulation of anti-apoptotic proteins and exhibited PARP cleavage. Gold-AbVF treated and GNP treated cells showed internalization of the nanoparticles in early and late endosomes and in multivesicular bodies. Non-coated gold nanoparticles alone were able to induce some levels of apoptosis in CLL B cells. This paper opens up new opportunities in the treatment of CLL-B using gold nanoparticles and integrates nanoscience with therapy in CLL. In future, potential opportunities exist to harness the optoelectronic properties of gold nanoparticles in the treatment of CLL

Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis

Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1–22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases