1,720,955 research outputs found
Enhancing Privacy and Security in Cloud-Based Machine Learning
No full textMachine Learning (ML) has been widely applied across various domains, including computer vision (CV), natural language processing (NLP), automatic speech recognition (ASR), and recommender systems (RS). The success of ML models largely depends on data availability and computational resources. For instance, ChatGPT , a widely used large language model, was trained on an extensive dataset derived from books, websites, and other text sources, encompassing approximately 570 GB of text data and containing a total of 175 billion parameters. Training such a model requires massive computational power, involving thousands of GPUs and consuming weeks or months of training time. Therefore, training a ML model is a challenging for users who lack access to sufficient data or computational resources. To address this issue, cloud-based ML - also known as ML as a Service (MLaaS) - has emerged as a scalable solution, enabling users to leverage pre-trained models or train custom models using cloud infrastructure.MLaaS offers two primary services: inference services (IS) and training services (TS). In IS, a client sends data to a cloud server hosting a pretrained model and receives prediction results. In TS, a client outsources model training to a cloud server with high computational capacity and obtains the trained model. Despite its advantages, MLaaS presents significant privacy and security challenges. In IS, client data often contains sensitive information; thus, exposing it to the server raises privacy concerns. In TS, the outsourced training process is vulnerable to backdoor attacks, where a malicious server implants hidden functionalities into the model, raising security concerns.While extensive research has been conducted on privacy and security in MLaaS, several research gaps remain. For privacy concerns in IS, prior works propose secure inference (also known as oblivious inference) methods, enabling clients to obtain predictions without exposing plain data to the server. Cryptographic techniques such as homomorphic encryption (HE) and secure multi-party computation (MPC) have been widely adopted in secure inference. However, these approaches suffer from high computational and communication overheads. This thesis introduces more efficient protocols that achieve a better balance between computational cost and communication efficiency for secure inference. For security concerns in TS, existing research extensively examines backdoor attacks in CV, NLP, and ASR, but their impact on RS remains unexplored. Furthermore, the recommender systems as a service (RaaS) paradigm is increasingly adopted, where e-commerce companies outsource RS model training to the cloud (e.g., Amazon Personalize). This thesis investigates backdoor attacks in RaaS and proposes robust mitigation strategies to enhance its security.By addressing both privacy and security challenges in MLaaS, this thesis contributes to a robust framework for mitigating privacy risks in IS and security threats in TS, making safer and more trustworthy MLaaS.</p
HeFUN: Homomorphic Encryption for Unconstrained Secure Neural Network Inference
No full textHomomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by three inherent limitations. Firstly, they cannot evaluate non-linear functions such as ReLU, the most widely adopted activation function in neural networks. Secondly, the permitted number of homomorphic operations on ciphertexts is bounded, consequently limiting the depth of neural networks that can be evaluated. Thirdly, the computational overhead associated with HE is prohibitively high, particularly for deep neural networks. In this paper, we introduce a novel paradigm designed to address the three limitations of HE-based SNNI. Our approach is an interactive approach that is solely based on HE, called iLHE. Utilizing the idea of iLHE, we present two protocols: ReLU, which facilitates the direct evaluation of the ReLU function on encrypted data, tackling the first limitation, and HeRefresh, which extends the feasible depth of neural network computations and mitigates the computational overhead, thereby addressing the second and third limitations. Based on HeReLU and HeRefresh protocols, we build a new framework for SNNI, named HeFUN. We prove that our protocols and the HeFUN framework are secure in the semi-honest security model. Empirical evaluations demonstrate that HeFUN surpasses current HE-based SNNI frameworks in multiple aspects, including security, accuracy, the number of communication rounds, and inference latency. Specifically, for a convolutional neural network with four layers on the MNIST dataset, HeFUN achieves 99.16% accuracy with an inference latency of 1.501 s, surpassing the popular HE-based framework CryptoNets proposed by Gilad-Bachrach, which achieves 98.52% accuracy with an inference latency of 3.479 s.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
HEArgmax: Secure homomorphic encryption-based protocols for Argmax function
No full textIn the current era of big data, cloud-based Machine Learning as a Service (MLaaS) – where clients send encrypted queries to the cloud and receive prediction results – has gained significant attention. However, privacy concerns arise as cloud servers typically require access to clients’ raw data, potentially exposing sensitive information. Homomorphic encryption (HE), an advanced cryptographic technique that allows computation on encrypted data without decryption, offers a promising foundation for privacy-preserving MLaaS. A critical challenge in this context is the efficient and secure evaluation of the argmax function—a key operation in classification tasks used to select the class with the highest predicted probability. Existing HE-based methods, such as Phoenix (Jovanovic et al., 2022), rely on non-interactive protocols using high-degree polynomial approximations of the sign function, which lead to significant computational overhead. This paper introduces HEArgmax, an interactive protocol designed for efficient and secure argmax evaluation under encryption. Unlike prior approaches, HEArgmax leverages the algebraic properties of the sign function in combination with a lightweight interactive mechanism under the standard semi-honest model, without requiring trusted setup or multi-party computation. We present two protocol variants: HEArgmax-HT, optimized for high-throughput scenarios using batch processing, and HEArgmax-LC, which minimizes communication by processing a single encrypted vector. Experiments show that HEArgmax reduces inference latency from 157 s to 8 s on the MNIST dataset, and performs well even on CIFAR-100 with 100 output classes, completing in under 4 min using 128-bit HE security parameters. Despite being interactive, our protocol achieves comparable communication costs to Phoenix. These results demonstrate that HEArgmax is both practical and scalable for real-world privacy-preserving MLaaS deployments.</p
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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