IEEE journal on selected areas in information theory最新文献_第6页

Differentially Private Sketch-and-Solve for Community Detection via Semidefinite Programming 通过半有限编程进行社群检测的差分私有化草图求解法

IEEE journal on selected areas in information theory Pub Date : 2024-03-06 DOI: 10.1109/JSAIT.2024.3396400

Mohamed Seif;Yanxi Chen;Andrea J. Goldsmith;H. Vincent Poor

引用次数: 0

Noisy Computing of the OR and MAX Functions OR 和 MAX 函数的噪声计算

IEEE journal on selected areas in information theory Pub Date : 2024-03-06 DOI: 10.1109/JSAIT.2024.3396787

Banghua Zhu;Ziao Wang;Nadim Ghaddar;Jiantao Jiao;Lele Wang

{"title":"Noisy Computing of the OR and MAX Functions","authors":"Banghua Zhu;Ziao Wang;Nadim Ghaddar;Jiantao Jiao;Lele Wang","doi":"10.1109/JSAIT.2024.3396787","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3396787","url":null,"abstract":"We consider the problem of computing a function of n variables using noisy queries, where each query is incorrect with some fixed and known probability \u0000<inline-formula> <tex-math>$p in (0,1/2)$ </tex-math></inline-formula>\u0000. Specifically, we consider the computation of the \u0000<inline-formula> <tex-math>$textsf {OR}$ </tex-math></inline-formula>\u0000 function of n bits (where queries correspond to noisy readings of the bits) and the \u0000<inline-formula> <tex-math>$textsf {MAX}$ </tex-math></inline-formula>\u0000 function of n real numbers (where queries correspond to noisy pairwise comparisons). We show that an expected number of queries of \u0000<inline-formula> <tex-math>$(1 pm o(1)) {}frac {nlog {}frac {1}{delta }}{D_{textsf {KL}}(p | 1-p)}$ </tex-math></inline-formula>\u0000 is both sufficient and necessary to compute both functions with a vanishing error probability \u0000<inline-formula> <tex-math>$delta = o(1)$ </tex-math></inline-formula>\u0000, where \u0000<inline-formula> <tex-math>$D_{textsf {KL}}(p | 1-p)$ </tex-math></inline-formula>\u0000 denotes the Kullback-Leibler divergence between \u0000<inline-formula> <tex-math>$textsf {Bern}(p)$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$textsf {Bern}(1-p)$ </tex-math></inline-formula>\u0000 distributions. Compared to previous work, our results tighten the dependence on p in both the upper and lower bounds for the two functions.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"302-313"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of Sparse Mixtures With Differential Privacy 利用差异隐私检测稀疏混合物

IEEE journal on selected areas in information theory Pub Date : 2024-03-06 DOI: 10.1109/JSAIT.2024.3396079

Ruizhi Zhang

{"title":"Detection of Sparse Mixtures With Differential Privacy","authors":"Ruizhi Zhang","doi":"10.1109/JSAIT.2024.3396079","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3396079","url":null,"abstract":"Detection of sparse signals arises in many modern applications such as signal processing, bioinformatics, finance, and disease surveillance. However, in many of these applications, the data may contain sensitive personal information, which is desirable to be protected during the data analysis. In this article, we consider the problem of \u0000<inline-formula> <tex-math>$(epsilon,delta)$ </tex-math></inline-formula>\u0000-differentially private detection of a general sparse mixture with a focus on how privacy affects the detection power. By investigating the nonasymptotic upper bound for the summation of error probabilities, we find any \u0000<inline-formula> <tex-math>$(epsilon,delta)$ </tex-math></inline-formula>\u0000-differentially private test cannot detect the sparse signal if the privacy constraint is too strong or if the model parameters are in the undetectable region (Cai and Wu, 2014). Moreover, we study the private clamped log-likelihood ratio test proposed by Canonne et al., 2019 and show it achieves vanishing error probabilities in some conditions on the model parameters and privacy parameters. Then, for the case when the null distribution is a standard normal distribution, we propose an adaptive \u0000<inline-formula> <tex-math>$(epsilon,delta)$ </tex-math></inline-formula>\u0000-differentially private test, which achieves vanishing error probabilities in the same detectable region (Cai and Wu, 2014) when the privacy parameters satisfy certain sufficient conditions. Several numerical experiments are conducted to verify our theoretical results and illustrate the performance of our proposed test.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"347-356"},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141435289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flow-Based Distributionally Robust Optimization 基于流量的分布式鲁棒优化

IEEE journal on selected areas in information theory Pub Date : 2024-02-27 DOI: 10.1109/JSAIT.2024.3370699

Chen Xu;Jonghyeok Lee;Xiuyuan Cheng;Yao Xie

{"title":"Flow-Based Distributionally Robust Optimization","authors":"Chen Xu;Jonghyeok Lee;Xiuyuan Cheng;Yao Xie","doi":"10.1109/JSAIT.2024.3370699","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3370699","url":null,"abstract":"We present a computationally efficient framework, called \u0000<monospace>FlowDRO</monospace>\u0000, for solving flow-based distributionally robust optimization (DRO) problems with Wasserstein uncertainty sets while aiming to find continuous worst-case distribution (also called the Least Favorable Distribution, LFD) and sample from it. The requirement for LFD to be continuous is so that the algorithm can be scalable to problems with larger sample sizes and achieve better generalization capability for the induced robust algorithms. To tackle the computationally challenging infinitely dimensional optimization problem, we leverage flow-based models and continuous-time invertible transport maps between the data distribution and the target distribution and develop a Wasserstein proximal gradient flow type algorithm. In theory, we establish the equivalence of the solution by optimal transport map to the original formulation, as well as the dual form of the problem through Wasserstein calculus and Brenier theorem. In practice, we parameterize the transport maps by a sequence of neural networks progressively trained in blocks by gradient descent. We demonstrate its usage in adversarial learning, distributionally robust hypothesis testing, and a new mechanism for data-driven distribution perturbation differential privacy, where the proposed method gives strong empirical performance on high-dimensional real data.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"62-77"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forking Uncertainties: Reliable Prediction and Model Predictive Control With Sequence Models via Conformal Risk Control 分叉不确定性：通过共形风险控制利用序列模型进行可靠预测和模型预测控制

IEEE journal on selected areas in information theory Pub Date : 2024-02-26 DOI: 10.1109/JSAIT.2024.3368229

Matteo Zecchin;Sangwoo Park;Osvaldo Simeone

{"title":"Forking Uncertainties: Reliable Prediction and Model Predictive Control With Sequence Models via Conformal Risk Control","authors":"Matteo Zecchin;Sangwoo Park;Osvaldo Simeone","doi":"10.1109/JSAIT.2024.3368229","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3368229","url":null,"abstract":"In many real-world problems, predictions are leveraged to monitor and control cyber-physical systems, demanding guarantees on the satisfaction of reliability and safety requirements. However, predictions are inherently uncertain, and managing prediction uncertainty presents significant challenges in environments characterized by complex dynamics and forking trajectories. In this work, we assume access to a pre-designed probabilistic implicit or explicit sequence model, which may have been obtained using model-based or model-free methods. We introduce probabilistic time series-conformal risk prediction (PTS-CRC), a novel post-hoc calibration procedure that operates on the predictions produced by any pre-designed probabilistic forecaster to yield reliable error bars. In contrast to existing art, PTS-CRC produces predictive sets based on an ensemble of multiple prototype trajectories sampled from the sequence model, supporting the efficient representation of forking uncertainties. Furthermore, unlike the state of the art, PTS-CRC can satisfy reliability definitions beyond coverage. This property is leveraged to devise a novel model predictive control (MPC) framework that addresses open-loop and closed-loop control problems under general average constraints on the quality or safety of the control policy. We experimentally validate the performance of PTS-CRC prediction and control by studying a number of use cases in the context of wireless networking. Across all the considered tasks, PTS-CRC predictors are shown to provide more informative predictive sets, as well as safe control policies with larger returns.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"44-61"},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Continual Mean Estimation Under User-Level Privacy 用户级隐私下的连续平均值估计

IEEE journal on selected areas in information theory Pub Date : 2024-02-22 DOI: 10.1109/JSAIT.2024.3366086

Anand Jerry George;Lekshmi Ramesh;Aditya Vikram Singh;Himanshu Tyagi

{"title":"Continual Mean Estimation Under User-Level Privacy","authors":"Anand Jerry George;Lekshmi Ramesh;Aditya Vikram Singh;Himanshu Tyagi","doi":"10.1109/JSAIT.2024.3366086","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3366086","url":null,"abstract":"We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary order. Until now these requirements of continual release and user-level privacy were considered in isolation. But, in practice, both these requirements come together as the users often contribute data repeatedly and multiple queries are made. We provide an algorithm that outputs a mean estimate at every time instant t such that the overall release is user-level \u0000<inline-formula> <tex-math>$varepsilon $ </tex-math></inline-formula>\u0000-DP and has the following error guarantee: Denoting by \u0000<inline-formula> <tex-math>$m_{t}$ </tex-math></inline-formula>\u0000 the maximum number of samples contributed by a user, as long as \u0000<inline-formula> <tex-math>$tilde {Omega }(1/varepsilon)$ </tex-math></inline-formula>\u0000 users have \u0000<inline-formula> <tex-math>$m_{t}/2$ </tex-math></inline-formula>\u0000 samples each, the error at time t is \u0000<inline-formula> <tex-math>$tilde {O}(1/sqrt {t}+sqrt {m}_{t}/tvarepsilon)$ </tex-math></inline-formula>\u0000. This is a universal error guarantee which is valid for all arrival patterns of the users. Furthermore, it (almost) matches the existing lower bounds for the single-release setting at all time instants when users have contributed equal number of samples.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"28-43"},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140297133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Message Shuffled Privacy in Federated Learning 联盟学习中的多信息洗牌隐私保护

IEEE journal on selected areas in information theory Pub Date : 2024-02-15 DOI: 10.1109/JSAIT.2024.3366225

Antonious M. Girgis;Suhas Diggavi

引用次数: 0

Quickest Change Detection With Controlled Sensing 通过受控传感技术实现最快速的变化检测

IEEE journal on selected areas in information theory Pub Date : 2024-02-06 DOI: 10.1109/JSAIT.2024.3362324

Venugopal V. Veeravalli;Georgios Fellouris;George V. Moustakides

{"title":"Quickest Change Detection With Controlled Sensing","authors":"Venugopal V. Veeravalli;Georgios Fellouris;George V. Moustakides","doi":"10.1109/JSAIT.2024.3362324","DOIUrl":"https://doi.org/10.1109/JSAIT.2024.3362324","url":null,"abstract":"In the problem of quickest change detection, a change occurs at some unknown time in the distribution of a sequence of random vectors that are monitored in real time, and the goal is to detect this change as quickly as possible subject to a certain false alarm constraint. In this work we consider this problem in the presence of parametric uncertainty in the post-change regime and controlled sensing. That is, the post-change distribution contains an unknown parameter, and the distribution of each observation, before and after the change, is affected by a control action. In this context, in addition to a stopping rule that determines the time at which it is declared that the change has occurred, one also needs to determine a sequential control policy, which chooses the control action at each time based on the already collected observations. We formulate this problem mathematically using Lorden’s minimax criterion, and assuming that there are finitely many possible actions and post-change parameter values. We then propose a specific procedure for this problem that employs an adaptive CuSum statistic in which (i) the estimate of the parameter is based on a fixed number of the more recent observations, and (ii) each action is selected to maximize the Kullback-Leibler divergence of the next observation based on the current parameter estimate, apart from a small number of exploration times. We show that this procedure, which we call the Windowed Chernoff-CuSum (WCC), is first-order asymptotically optimal under Lorden’s minimax criterion, for every possible value of the unknown post-change parameter, as the mean time to false alarm goes to infinity. We also provide simulation results to illustrate the performance of the WCC procedure.","PeriodicalId":73295,"journal":{"name":"IEEE journal on selected areas in information theory","volume":"5 ","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10423411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140052897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pull or Wait: How to Optimize Query Age of Information 拉动或等待：如何优化查询信息时代

IEEE journal on selected areas in information theory Pub Date : 2023-12-29 DOI: 10.1109/JSAIT.2023.3346308

M. Emrullah Ildiz;Orhan T. Yavascan;Elif Uysal;O. Tugberk Kartal

引用次数: 0

Optimal Update Times for Stale Information Metrics Including the Age of Information 包括信息时代在内的陈旧信息指标的最佳更新时间

IEEE journal on selected areas in information theory Pub Date : 2023-12-22 DOI: 10.1109/JSAIT.2023.3344760

Chris Ferguson;Leonard Kleinrock

引用次数: 0