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

Deep Geometrized Cartoon Line Inbetweening

We aim to address a significant but understudied problem in the anime industry, namely the inbetweening of cartoon line drawings. Inbetweening involves generating intermediate frames between two black-and-white line drawings and is a time-consuming and expensive process that can benefit from automation. However, existing frame interpolation methods that rely on matching and warping whole raster images are unsuitable for line inbetweening and often produce blurring artifacts that damage the intricate line structures. To preserve the precision and detail of the line drawings, we propose a new approach, AnimeInbet, which geometrizes raster line drawings into graphs of endpoints and reframes the inbetweening task as a graph fusion problem with vertex repositioning. Our method can effectively capture the sparsity and unique structure of line drawings while preserving the details during inbetweening. This is made possible via our novel modules, i.e., vertex geometric embedding, a vertex correspondence Transformer, an effective mechanism for vertex repositioning and a visibility predictor. To train our method, we introduce MixamoLine240, a new dataset of line drawings with ground truth vectorization and matching labels. Our experiments demonstrate that AnimeInbet synthesizes high-quality, clean, and complete intermediate line drawings, outperforming existing methods quantitatively and qualitatively, especially in cases with large motions. Data and code are available at https://github.com/lisiyao21/AnimeInbet.Comment: ICCV 202

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

PPAR-α Agonist Fenofibrate Upregulates Tetrahydrobiopterin Level through Increasing the Expression of Guanosine 5′-Triphosphate Cyclohydrolase-I in Human Umbilical Vein Endothelial Cells

Tetrahydrobiopterin (BH4) is an essential cofactor for endothelial nitric oxide (NO) synthase. Guanosine 5′-triphosphate cyclohydrolase-I (GTPCH-I) is a key limiting enzyme for BH4 synthesis. In the present in vitro study, we investigated whether peroxisome proliferator-activated receptor α (PPAR-α) agonist fenofibrate could recouple eNOS by reversing low-expression of intracellular BH4 in endothelial cells and discussed the potential mechanisms. After human umbilical vein endothelial cells (HUVECs) were treated with lipopolysaccharide (LPS) for 24 hours, the levels of cellular eNOS, BH4 and cell supernatant NO were significantly reduced compared to control group. And the fluorescence intensity of intracellular ROS was significantly increased. But pretreated with fenofibrate (10 umol/L) for 2 hours before cells were induced by LPS, the levels of eNOS, NO, and BH4 were significantly raised compared to LPS treatment alone. ROS production was markedly reduced in fenofibrate group than LPS group. In addition, our results showed that the level of intracellular GTPCH-I detected by western blot was increased in a concentration-dependent manner after being treated with fenofibrate. These results suggested that fenofibrate might help protect endothelial function and against atherosclerosis by increasing level of BH4 and decreasing production of ROS through upregulating the level of intracellular GTPCH-I

Synthetic engineering of a new biocatalyst encapsulating [NiFe]-hydrogenases for enhanced hydrogen production

Hydrogenases are microbial metalloenzymes capable of catalyzing the reversible interconversion between molecular hydrogen and protons with high efficiency, and have great potential in the development of new electrocatalysts for renewable...</jats:p

Reprogramming bacterial protein organelles as a nanoreactor for hydrogen production

Compartmentalization is a ubiquitous building principle in cells, which permits segregation of biological elements and reactions. The carboxysome is a specialized bacterial organelle that encapsulates enzymes into a virus-like protein shell and plays essential roles in photosynthetic carbon fixation. The naturally designed architecture, semi-permeability, and catalytic improvement of carboxysomes have inspired rational design and engineering of new nanomaterials to incorporate desired enzymes into the protein shell for enhanced catalytic performance. Here, we build large, intact carboxysome shells (over 90 nm in diameter) in the industrial microorganism Escherichia coli by expressing a set of carboxysome protein-encoding genes. We develop strategies for enzyme activation, shell self-assembly, and cargo encapsulation to construct a robust nanoreactor that incorporates catalytically active [FeFe]-hydrogenases and functional partners within the empty shell for the production of hydrogen. We show that shell encapsulation and the internal microenvironment of the new catalyst facilitate hydrogen production of the encapsulated oxygen-sensitive hydrogenases. The study provides insights into the assembly and formation of carboxysomes and paves the way for engineering carboxysome shell-based nanoreactors to recruit specific enzymes for diverse catalytic reactions

Ten years of algal biofuel and bioproducts: gains and pains

Main conclusion It has been proposed that future efforts should focus on basic studies, biotechnology studies and synthetic biology studies related to algal biofuels and various high-value bioproducts for the economically viable production of algal biof uels. In recognition of diminishing fossil fuel reserves and the worsening environment, microalgal biofuel has been proposed as a renewable energy source with great potential. Algal biofuel thus became one of the hottest topics in renewable energy research in the new century, especially over the past decade. Between 2007 and 2017, research related to microalgal biofuels experienced a dramatic, three-stage development, rising, growing exponentially, and then declining rapidly due to overheating of the subject. However, biofuel-driven algal biotechnology and bioproducts research has been thriving since 2010. To clarify the gains (and pains) of the past decade and detail prospects for the future, this review summarizes the extensive scientific progress and substantial technical advances in algal biofuel over the past decade, covering basic biology, applied research, as well as the production of value-added natural products. Even after 10 years of hard work and billions of dollars in investments, its unacceptably high cost remains the ultimate bottleneck for the industrialization of algal biofuel. To maximize the total research benefits, both economically and socially, it has been proposed that future efforts should focus on basic studies to characterize oilgae, on biotechnology studies into various high-value bioproducts. Moreover, the development of synthetic biology provides new possibilities for the economically viable production of biofuels via the directional manufacture of microalgal bioproducts in algal cell factories

A Joint Location–Allocation–Inventory Spare Part Optimization Model for Base-Level Support System with Uncertain Demands

This paper copes with a joint Location-Allocation-Inventory problem in a three-echelon base-level spare part support system with epistemic uncertainty in uncertain demands of bases. The aim of the paper is to propose an optimization model under the uncertainty theory to minimize the total cost, which integrates crucial characterizations of the inventory control decisions and the location-allocation scheme arrangement under a periodic review order-up-to-S (T, S) policy. Uncertainty theory is introduced in this paper to characterize epistemic uncertainty, where demands are treated as uncertain variables and stockout loss is represented by value-at-risk in uncertain measurement. To solve the original uncertain optimization model, an equivalent deterministic model is derived and addressed by an improved bilevel genetic algorithm. Moreover, the proposed models and algorithm are encoded into numerical examples for supply chain programming. The results highlight the applicability of the model and the algorithm’s effectiveness in approaching the optimal solution compared with traditional genetic algorithm. Sensitivity analyses are further made for the impacts of review time and inventory capacity on different cost components