標(biāo)題: Titlebook: Differential Privacy and Applications; Tianqing Zhu,Gang Li,Philip S. Yu Book 2017 Springer International Publishing AG 2017 data analysis [打印本頁(yè)] 作者: 去是公開(kāi) 時(shí)間: 2025-3-21 16:22
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書目名稱Differential Privacy and Applications影響因子(影響力)學(xué)科排名
書目名稱Differential Privacy and Applications網(wǎng)絡(luò)公開(kāi)度
書目名稱Differential Privacy and Applications網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Differential Privacy and Applications被引頻次
書目名稱Differential Privacy and Applications被引頻次學(xué)科排名
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書目名稱Differential Privacy and Applications讀者反饋
書目名稱Differential Privacy and Applications讀者反饋學(xué)科排名
作者: creatine-kinase 時(shí)間: 2025-3-21 20:38
https://doi.org/10.1007/978-3-031-56188-7 bound or sample complexity. But private learning frameworks can only deal with limited learning algorithms, while nearly all types of analysis algorithms can be implemented in a Laplace/exponential framework.作者: 無(wú)節(jié)奏 時(shí)間: 2025-3-22 01:13 作者: 得意人 時(shí)間: 2025-3-22 05:06
Lisa Wiebesiek,Relebohile Moletsaner of queries is limited, as a large volume of noise will be introduced when the number of queries increases. A method called graph update method is then presented in this chapter to solve this serious problem. The key idea of the method is to transfer the query release problem into an iteration proc作者: PHAG 時(shí)間: 2025-3-22 10:56
Alexandra Budke,Kimberley Hindmarshms and utilize differential privacy to prevent the leaking of private information when releasing the dataset. A private tagging release algorithm is presented in this chapter to provide comprehensive privacy-preserving capability for individuals and maximizing the utility of the released dataset. Th作者: 直覺(jué)好 時(shí)間: 2025-3-22 14:01 作者: 直覺(jué)好 時(shí)間: 2025-3-22 20:15 作者: 全等 時(shí)間: 2025-3-22 22:19 作者: 為現(xiàn)場(chǎng) 時(shí)間: 2025-3-23 03:23 作者: considerable 時(shí)間: 2025-3-23 08:26
Differentially Private Deep Learning,uted Private SGD. Each of them is focusing on a particular deep learning algorithm and is dealing with those two challenges in different ways. Finally, this chapter shows several popular datasets that can be used in differentially private deep learning.作者: entrance 時(shí)間: 2025-3-23 09:49 作者: Uncultured 時(shí)間: 2025-3-23 15:20
Privacy Preserving for Tagging Recommender Systems,ms and utilize differential privacy to prevent the leaking of private information when releasing the dataset. A private tagging release algorithm is presented in this chapter to provide comprehensive privacy-preserving capability for individuals and maximizing the utility of the released dataset. Th作者: transplantation 時(shí)間: 2025-3-23 21:15 作者: 強(qiáng)制令 時(shí)間: 2025-3-23 23:29
Differentially Private Spatial Crowdsourcing,e privacy breaches. In recent years there have been a number of proposals to provide the privacy preserving capability for SC applications, such as allowing the release of spatial datasets while preserving privacy. This chapter first surveys the current attempts to solve the location privacy problem作者: 幸福愉悅感 時(shí)間: 2025-3-24 05:39
Preliminary of Differential Privacy,, local sensitivity, and principle mechanisms that can preserve differential privacy. To make the theory accessible, an example is proposed to illustrate these concepts. In addition, utility measurements are discussed in this chapter.作者: 珠寶 時(shí)間: 2025-3-24 07:40 作者: 天真 時(shí)間: 2025-3-24 13:17
Re-thinking Religious Pluralism, local sensitivity, and principle mechanisms that can preserve differential privacy. To make the theory accessible, an example is proposed to illustrate these concepts. In addition, utility measurements are discussed in this chapter.作者: SENT 時(shí)間: 2025-3-24 18:34
https://doi.org/10.1007/978-3-319-62004-6data analysis; data mining; data release; differential policy; location privacy; machine learning; privacy作者: 冒失 時(shí)間: 2025-3-24 20:15
978-3-319-87211-7Springer International Publishing AG 2017作者: 性別 時(shí)間: 2025-3-25 02:21 作者: blackout 時(shí)間: 2025-3-25 03:44 作者: absolve 時(shí)間: 2025-3-25 08:48
Re-thinking Religious Pluralismhe speed of such data collection has increased dramatically over the last a few years. However, most of the collected datasets are personally related and contain private or sensitive information. Differential privacy is a solid privacy model that provides a provable privacy guarantee for individuals作者: 繁榮地區(qū) 時(shí)間: 2025-3-25 15:38 作者: 后天習(xí)得 時(shí)間: 2025-3-25 16:30 作者: Brain-Imaging 時(shí)間: 2025-3-25 21:01 作者: Customary 時(shí)間: 2025-3-26 01:58
Palgrave Studies in Literary Journalismtaset publishing. Non-interactive settings mean all queries are given to the curator at one time. The key challenge for non-interactive publishing is the sensitivity measurement. Correlation between queries will dramatically increase the sensitivity. Two possible methods are proposed to fix this pro作者: 斷斷續(xù)續(xù) 時(shí)間: 2025-3-26 07:18 作者: 松果 時(shí)間: 2025-3-26 10:57 作者: WITH 時(shí)間: 2025-3-26 14:45
Sarah Flicker,Maureen Owino,Katie MacEnteeroblem for a particular application are shown in the first figure of this chapter. The dark boxes in the flowchart show the steps, and the orange boxes illustrate the possible choices. First, it is necessary to identify the scenarios: data publishing or data analysis. Data publishing aims to release作者: 摘要 時(shí)間: 2025-3-26 17:29 作者: 巧思 時(shí)間: 2025-3-27 00:31 作者: 失望未來(lái) 時(shí)間: 2025-3-27 01:06
Alexandra Budke,Kimberley Hindmarshs for a resource. They combine the advantages of automation in traditional recommender systems and flexibility of tagging systems. A large collection of data has been generated by those social network web sites with tagging recommender systems during the last few years, and the issue of privacy in t作者: CROW 時(shí)間: 2025-3-27 08:44 作者: 腫塊 時(shí)間: 2025-3-27 10:06
https://doi.org/10.1007/978-3-031-40747-5 can perform tasks at specified locations in return for rewards offered by employers. Existing methods ensure the efficiency of their systems by submitting the workers’ exact locations to a centralized server for task assignment, which can lead to privacy violations. Thus, implementing crowsourcing 作者: AMITY 時(shí)間: 2025-3-27 14:47 作者: 內(nèi)部 時(shí)間: 2025-3-27 19:51
Transferred Intuitions. Scripted Routinesising issues remain unexplored. The development of social networks provides great opportunities for research on privacy-preserving but also presents a challenge in effective utilization of the large volume of data. There are still other topics that need to be considered in differential privacy, and 作者: 我不明白 時(shí)間: 2025-3-27 22:37
1568-2633 ring rather than computing theory.Includes examples on vario.This book focuses on differential privacy and its application with an emphasis on technical and application aspects. This book also presents the most recent research on differential privacy with a theory perspective. It provides an approac作者: 美麗的寫 時(shí)間: 2025-3-28 05:23
Now Everyone’s a Travel Writer? previous query has been published. In the non-interactive setting, all queries are given to the curator at one time. The curator can provide answers with full knowledge of the query set. This chapter focuses on the interactive setting in the data publishing.作者: Maximizer 時(shí)間: 2025-3-28 08:43 作者: 出處 時(shí)間: 2025-3-28 12:18 作者: nuclear-tests 時(shí)間: 2025-3-28 16:37 作者: MONY 時(shí)間: 2025-3-28 22:07 作者: TRACE 時(shí)間: 2025-3-29 01:27 作者: 誤傳 時(shí)間: 2025-3-29 04:26
Transferred Intuitions. Scripted Routineswe consider a few directions, including adaptive data analysis, personalized privacy, multiparty computation, differentially private mechanism design, private genetic data, local differential privacy and learning model publishing.作者: HILAR 時(shí)間: 2025-3-29 07:42
Book 2017 recent research on differential privacy with a theory perspective. It provides an approachable strategy for researchers and engineers to implement differential privacy in real world applications..Early chapters are focused on two major directions, differentially private data publishing and differen作者: ACTIN 時(shí)間: 2025-3-29 15:01 作者: 軍火 時(shí)間: 2025-3-29 16:50
Correlated Differential Privacy for Non-IID Datasets,correlated differential privacy problem and outlines the research issues and challenges in providing privacy guarantees for correlated datasets. Then it presents an innovative solution to solve the correlated differential privacy problem and shows that the solution is robust and effective.作者: Immunization 時(shí)間: 2025-3-29 22:03 作者: disciplined 時(shí)間: 2025-3-30 00:52
Differentially Private Data Publishing: Non-interactive Setting,blem: one is decomposing the correlation between batch queries and another is publishing a synthetic dataset with the constraint of differential privacy to answer those proposed queries. Related methods are presented in the synthetic dataset publishing Sections.作者: arabesque 時(shí)間: 2025-3-30 05:10 作者: STYX 時(shí)間: 2025-3-30 10:51
Sarah Flicker,Maureen Owino,Katie MacEnteeifying challenges in the application. Although differential privacy is considered to be a promising solution for privacy preservation issues, implementation in some applications still presents a number of challenges. These challenges, and their possible solutions, are introduced in the next subsection.作者: ATOPY 時(shí)間: 2025-3-30 15:28 作者: 使聲音降低 時(shí)間: 2025-3-30 19:12
Differentially Private Applications: Where to Start?,ifying challenges in the application. Although differential privacy is considered to be a promising solution for privacy preservation issues, implementation in some applications still presents a number of challenges. These challenges, and their possible solutions, are introduced in the next subsection.作者: RLS898 時(shí)間: 2025-3-30 22:24
Differentially Private Recommender System,privacy in providing privacy preserving capabilities for building differentially private recommender systems: (1) differentially private untrustworthy recommender system, (2) differentially private trustworthy recommender system, and (3) private neighborhood-based collaborative filtering (PNCF) method, with an emphasis on the PNCF method.作者: 賞錢 時(shí)間: 2025-3-31 04:00 作者: 襲擊 時(shí)間: 2025-3-31 07:28
Preliminary of Differential Privacy,, local sensitivity, and principle mechanisms that can preserve differential privacy. To make the theory accessible, an example is proposed to illustrate these concepts. In addition, utility measurements are discussed in this chapter.作者: Dorsal-Kyphosis 時(shí)間: 2025-3-31 12:39
Differentially Private Data Publishing: Settings and Mechanisms,teractive and non-interactive, are involved in this publishing scenario. In the interactive setting, a query can not be issued until the answer to the previous query has been published. In the non-interactive setting, all queries are given to the curator at one time. The curator can provide answers 作者: 鎮(zhèn)壓 時(shí)間: 2025-3-31 15:48
Differentially Private Data Publishing: Interactive Setting,ses publishing scenarios involving these types of input data. In interactive settings, the privacy mechanism receives a user’s query and replies with a noisy answer to preserve privacy. Traditional Laplace mechanisms can only answer sublinear of . queries, which is insufficient in many scenarios. Di
