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Ensuring the data Security and Integrity over Cloud Computing Environment using Novel Cipher Strategy

Ensuring the Data Security and Integrity over Cloud Computin...

2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Praveen Kumar, E. Yasotha, B. Narmadha, P.G. Chandrakala, P. Ushapriya, C. Chamundeeswari, G. Tamil Nadu Coimbatore India Srmist Department of Data Science and Business Systems Tamil Nadu kattankulathur India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India St. Martin's Engineering College Department of Artificial Intelligence and Machine Learning Telangana India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350303452

The advent of cloud computing has revolutionized the Internet. Users may effortlessly collaborate, back up, and access their information from any location thanks to cloud computing. When it comes to providing IT enabled services via the internet, cloud computing is the clear winner. Cloud computing allows a large number of people to share resources like computers, programs, and servers. Users of cloud services often have no idea where their data actually resides or where it came from. Users of cloud computing services are understandably concerned about the safety of their data once it has been uploaded to the cloud with respect to service providers. Privilege control, comprehensive encryption algorithms, management of keys, provenance instances and global consistency verification are only some of the numerous data privacy and security issues that occur as a result. This study introduces a novel cloud security strategy, Cloud based Novel Cipher Strategy (CNCS), and analyzes issues related to the safety and integrity of data stored in the cloud. We're looking into many data safety related factors here. In this article, we'll look closely at a selection of data protection strategies now in use throughout the globe to guarantee the highest level of security possible. Later, the study delves into the various methods of cloud based data security. To prevent unwanted access, alteration, or deletion of dynamic as well as static information, a procedure for validating their integrity is outlined. The proposed system CNCS efficiency is evaluated in the resulting section through the following metrics such as: integrity level, data storage efficiency and security level. All these metrics are evaluated based on the size of data, in which it is tested with respect to number of days the proposed scheme is tested over the industrial environment to prove the performance and efficiency of the proposed approach. © 2023 IEEE.

关键词： Cloud security

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DISCO: Internal Evaluation of Density-Based Clustering

arXiv

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arXiv 2025年

作者： Beer, Anna Krieger, Lena Weber, Pascal Ritzert, Martin Assent, Ira Plant, Claudia Faculty of Computer Science University of Vienna Vienna Austria IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Jülich Germany UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria Institute of Computer Science and Campus Institute Data Science University of Göttingen Göttingen Germany Department of Computer Science Aarhus University Aarhus Denmark

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels. © 2025, CC BY.

关键词： Cluster analysis

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Monitoring AI-modified content at scale: a case study on the impact of ChatGPT on AI conference peer reviews 24

Monitoring AI-modified content at scale: a case study on the...

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Proceedings of the 41st International Conference on machine learning

作者： Weixin Liang Zachary Izzo Yaohui Zhang Haley Lepp Hancheng Cao Xuandong Zhao Lingjiao Chen Haotian Ye Sheng Liu Zhi Huang Daniel A. McFarland James Y. Zou Department of Computer Science Stanford University Machine Learning Department NEC Labs America Department of Electrical Engineering Stanford University Graduate School of Education Stanford University Department of Computer Science and Department of Management Science and Engineering Stanford University Department of Computer Science UC Santa Barbara Department of Biomedical Data Science Stanford University Graduate School of Education and Department of Sociology and Graduate School of Business Stanford University Department of Computer Science and Department of Electrical Engineering and Department of Biomedical Data Science Stanford University

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

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S$^\text{3}$Attention: Improving Long Sequence Attention With Smoothed Skeleton Sketching

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IEEE Journal of Selected Topics in Signal Processing 2024年第6期18卷 985-996页

作者： Xue Wang Tian Zhou Jianqing Zhu Jialin Liu Kun Yuan Tao Yao Wotao Yin Rong Jin HanQin Cai Alibaba Group Bellevue WA USA Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Statistics and Data Science University of Central Florida Orlando FL USA Center for Machine Learning Research Peking University Beijing China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai China Meta Menlo Park CA USA Department of Statistics and Data Science and the Department of Computer Science University of Central Florida Orlando FL USA

Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S $^{3}$ Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S $^{3}$ Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S $^{3}$ Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S $^{3}$ Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures.

关键词： Computational modeling Complexity theory Transformers Task analysis Skeleton Kernel Noise

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Generalized independent noise condition for estimating causal structure with latent variables

The Journal of Machine Learning Research

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The Journal of machine learning Research 2024年第1期25卷 9044-9104页

作者： Feng Xie Biwei Huang Zhengming Chen Ruichu Cai Clark Glymour Zhi Geng Kun Zhang Department of Applied Statistics Beijing Technology and Business University Beijing China Halicioglu Data Science Institute (HDSI) University of California San Diego La Jolla San Diego California School of Computer Science Guangdong University of Technology Guangzhou China and Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE School of Computer Science Guangdong University of Technology Guangzhou China Department of Philosophy Carnegie Mellon University Pittsburgh PA Department of Philosophy Carnegie Mellon University Pittsburgh PA and Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE

We investigate the challenging task of learning causal structure in the presence of latent variables, including locating latent variables, determining their quantity, and identifying causal relationships among both latent and observed variables. To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables. Specifically, for two observed random vectors Y and Z, GIN holds if and only if ωΤ-Y and Z are statistically independent, where ω is a non-zero parameter vector determined by the cross-covariance between Y and Z. We then give necessary and sufficient graphical criteria of the GIN condition in linear non-Gaussian acyclic causal models. From a graphical perspective, roughly speaking, GIN implies the existence of a set S such that S is causally earlier (w.r.t. the causal ordering) than Y, and that every active (collider-free) path between Y and Z must contain a node from S. Interestingly, we find that the independent noise condition (i.e., if there is no confounder, causes are independent of the residual derived from regressing the effect on the causes) can be seen as a special case of GIN. With such a connection between GIN and latent causal structures, we further leverage the proposed GIN condition, together with a well-designed search procedure, to efficiently estimate Linear, Non-Gaussian Latent Hierarchical Models (LiNGLaHs), where latent confounders may also be causally related and may even follow a hierarchical structure. We show that the underlying causal structure of a LiNGLaH is identifiable in light of GIN conditions under mild assumptions. Experimental results on both synthetic and three real-world data sets show the effectiveness of the proposed approach.

关键词： causal discovery latent variable latent hierarchical structure latent causal graph non-Gaussianity

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Catoni-style confidence sequences for heavy-tailed mean estimation

arXiv

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arXiv 2022年

作者： Wang, Hongjian Ramdas, Aaditya Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States

A confidence sequence (CS) is a sequence of confidence intervals that is valid at arbitrary data-dependent stopping times. These are useful in applications like A/B testing, multi-armed bandits, off-policy evaluation, election auditing, etc. We present three approaches to constructing a confidence sequence for the population mean, under the minimal assumption that only an upper bound σ2 on the variance is known. While previous works rely on light-tail assumptions like boundedness or subGaussianity (under which all moments of a distribution exist), the confidence sequences in our work are able to handle data from a wide range of heavy-tailed distributions. The best among our three methods — the Catoni-style confidence sequence — performs remarkably well in practice, essentially matching the state-of-the-art methods for σ2-subGaussian data, and provably attains the plog log t/t lower bound due to the law of the iterated logarithm. Our findings have important implications for sequential experimentation with unbounded observations, since the σ2-bounded-variance assumption is more realistic and easier to verify than σ2-subGaussianity (which implies the former). We also extend our methods to data with infinite variance, but having pth central moment (1 MSC Codes 62L12, 60G42 Copyright © 2022, The Authors. All rights reserved.

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A permutation-free kernel two-sample test

arXiv

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arXiv 2022年

作者： Shekhar, Shubhanshu Kim, Ilmun Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Department of Statistics and Data Science Yonsei University Korea Republic of

The kernel Maximum Mean Discrepancy (MMD) is a popular multivariate distance metric between distributions that has found utility in two-sample testing. The usual kernel-MMD test statistic is a degenerate U-statistic under the null, and thus it has an intractable limiting distribution. Hence, to design a level-α test, one usually selects the rejection threshold as the (1−α)-quantile of the permutation distribution. The resulting nonparametric test has finite-sample validity but suffers from large computational cost, since every permutation takes quadratic time. We propose the cross-MMD, a new quadratic-time MMD test statistic based on sample-splitting and studentization. We prove that under mild assumptions, the cross-MMD has a limiting standard Gaussian distribution under the null. Importantly, we also show that the resulting test is consistent against any fixed alternative, and when using the Gaussian kernel, it has minimax rate-optimal power against local alternatives. For large sample sizes, our new cross-MMD provides a significant speedup over the MMD, for only a slight loss in power. © 2022, CC BY.

关键词： Sampling

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Risk-limiting Financial Audits via Weighted Sampling without Replacement

arXiv

引用

arXiv 2023年

作者： Shekhar, Shubhanshu Xu, Ziyu Lipton, Zachary C. Liang, Pierre J. Ramdas, Aaditya Department of Statistics and Data Science Carnegie Mellon University United States Machine Learning Department Carnegie Mellon University United States Tepper School of Business Carnegie Mellon University United States

We introduce the notion of a risk-limiting financial auditing (RLFA): given N transactions, the goal is to estimate the total misstated monetary fraction (m∗) to a given accuracy ϵ, with confidence 1-δ. We do this by constructing new confidence sequences (CSs) for the weighted average of N unknown values, based on samples drawn without replacement according to a (randomized) weighted sampling scheme. Using the idea of importance weighting to construct test martingales, we first develop a framework to construct CSs for arbitrary sampling strategies. Next, we develop methods to improve the quality of CSs by incorporating side information about the unknown values associated with each item. We show that when the side information is sufficiently predictive, it can directly drive the sampling. Addressing the case where the accuracy is unknown a priori, we introduce a method that incorporates side information via control variates. Crucially, our construction is adaptive: if the side information is highly predictive of the unknown misstated amounts, then the benefits of incorporating it are significant;but if the side information is uncorrelated, our methods learn to ignore it. Our methods recover state-of-the-art bounds for the special case when the weights are equal, which has already found applications in election auditing. The harder weighted case solves our more challenging problem of AI-assisted financial auditing. © 2023, CC BY.

关键词： Risk perception

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Well-posedness of an evaporation model for a spherical droplet exposed to an air flow

arXiv

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arXiv 2023年

作者： Bänsch, Eberhard Doß, Martin Gräser, Carsten Ray, Nadja Department of Mathematics Friedrich-Alexander University Erlangen-Nürnberg Erlangen Germany Mathematical Institute for Machine Learning and Data Science Catholic University Eichstätt-Ingolstadt Ingolstadt Germany

MSC Codes 35Q79 (Primary) 35A01, 35K55, 80A19, 80M10 (Secondary)In this paper, we address the well-posedness of an evaporation model for a spherical liquid droplet taking into account the convective impact of an air flow in the ambient gas phase. From a mathematical perspective, we are dealing with a coupled ODE-PDE system for the droplet radius, the temperature distribution, and the vapor concentration. The nonlinear coupling arises from the evaporation rate modeled by the Hertz-Knudsen equation. Under physically meaningful assumptions, we prove existence and uniqueness of a weak solution until the droplet has evaporated completely. Numerical simulations are performed to illustrate how different air flows affect the evaporation process. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical models

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PASS: Peer-Agreement based Sample Selection for Training with Noisy Labels

arXiv

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arXiv 2023年

作者： Garg, Arpit Nguyen, Cuong Felix, Rafael Do, Thanh-Toan Carneiro, Gustavo Australian Institute for Machine Learning University of Adelaide Australia Centre for Vision Speech and Signal Processing University of Surrey United Kingdom Department of Data Science and AI Monash University Australia

The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with noisy-label (LNL) techniques that focus on separating noisy- and clean-label samples to apply different learning strategies to each group of samples. Current methodologies often rely on the small-loss hypothesis or feature-based selection to separate noisy- and clean-label samples, yet our empirical observations reveal their limitations, especially for labels with instance dependent noise (IDN). An important characteristic of IDN is the difficulty to distinguish the clean-label samples that lie near the decision boundary (i.e., the hard samples) from the noisy-label samples. We, therefore, propose a new noisy-label detection method, termed Peer-Agreement based Sample Selection (PASS), to address this problem. Utilising a trio of classifiers, PASS employs consensus-driven peer-based agreement of two models to select the samples to train the remaining model. PASS is easily integrated into existing LNL models, enabling the improvement of the detection accuracy of noisy- and clean-label samples, which increases the classification accuracy across various LNL benchmarks. 1 Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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