21st International Conference on Data Engineering (ICDE'05)最新文献_第3页

Odysseus: a high-performance ORDBMS tightly-coupled with IR features 奥德修斯:一个高性能ORDBMS与IR特性紧密耦合

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.95

K. Whang, Min-Jae Lee, Jae-Gil Lee, Min-Soo Kim, Wook-Shin Han

引用次数: 30

Reverse nearest neighbors in large graphs 在大图中反转最近邻

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.124

Man Lung Yiu, D. Papadias, N. Mamoulis, Yufei Tao

引用次数: 43

Data privacy through optimal k-anonymization 通过最优k-匿名化实现数据隐私

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.42

R. Bayardo, R. Agrawal

{"title":"Data privacy through optimal k-anonymization","authors":"R. Bayardo, R. Agrawal","doi":"10.1109/ICDE.2005.42","DOIUrl":"https://doi.org/10.1109/ICDE.2005.42","url":null,"abstract":"Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122838802","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: 1327

Filter based directory replication and caching 基于过滤器的目录复制和缓存

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.67

Apurva Kumar

引用次数: 3

On the signature trees and balanced signature trees 关于签名树和平衡签名树

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.99

Yangjun Chen

引用次数: 11

Privacy and ownership preserving of outsourced medical data 外包医疗数据的隐私和所有权保护

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.111

E. Bertino, B. Ooi, Yanjiang Yang, R. Deng

{"title":"Privacy and ownership preserving of outsourced medical data","authors":"E. Bertino, B. Ooi, Yanjiang Yang, R. Deng","doi":"10.1109/ICDE.2005.111","DOIUrl":"https://doi.org/10.1109/ICDE.2005.111","url":null,"abstract":"The demand for the secondary use of medical data is increasing steadily to allow for the provision of better quality health care. Two important issues pertaining to this sharing of data have to be addressed: one is the privacy protection for individuals referred to in the data; the other is copyright protection over the data. In this paper, we present a unified framework that seamlessly combines techniques of binning and digital watermarking to attain the dual goals of privacy and copyright protection. Our binning method is built upon an earlier approach of generalization and suppression by allowing a broader concept of generalization. To ensure data usefulness, we propose constraining binning by usage metrics that define maximal allowable information loss, and the metrics can be enforced off-line. Our watermarking algorithm watermarks the binned data in a hierarchical manner by leveraging on the very nature of the data. The method is resilient to the generalization attack that is specific to the binned data, as well as other attacks intended to destroy the inserted mark. We prove that watermarking could not adversely interfere with binning, and implemented the framework. Experiments were conducted, and the results show the robustness of the proposed framework.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132299152","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: 159

On the optimal ordering of maps and selections under factorization 分解下映射的最优排序与选择

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.97

Thomas Neumann, S. Helmer, G. Moerkotte

引用次数: 15

Efficient processing of skyline queries with partially-ordered domains 具有部分有序域的天际线查询的高效处理

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.60

C. Chan, P. Eng, K. Tan

引用次数: 30

Effective computation of biased quantiles over data streams 有效计算数据流上的偏分位数

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.55

Graham Cormode, Flip Korn, S. Muthukrishnan, D. Srivastava

{"title":"Effective computation of biased quantiles over data streams","authors":"Graham Cormode, Flip Korn, S. Muthukrishnan, D. Srivastava","doi":"10.1109/ICDE.2005.55","DOIUrl":"https://doi.org/10.1109/ICDE.2005.55","url":null,"abstract":"Skew is prevalent in many data sources such as IP traffic streams. To continually summarize the distribution of such data, a high-biased set of quantiles (e.g., 50th, 90th and 99th percentiles) with finer error guarantees at higher ranks (e.g., errors of 5, 1 and 0.1 percent, respectively) is more useful than uniformly distributed quantiles (e.g., 25th, 50th and 75th percentiles) with uniform error guarantees. In this paper, we address the following two problems. First, can we compute quantiles with finer error guarantees for the higher ranks of the data distribution effectively using less space and computation time than computing all quantiles uniformly at the finest error? Second, if specific quantiles and their error bounds are requested a priori, can the necessary space usage and computation time be reduced? We answer both questions in the affirmative by formalizing them as the \"high-biased\" and the \"targeted\" quantiles problems, respectively, and presenting algorithms with provable guarantees, that perform significantly better than previously known solutions for these problems. We implemented our algorithms in the Gigascope data stream management system, and evaluated alternate approaches for maintaining the relevant summary structures. Our experimental results on real and synthetic IP data streams complement our theoretical analyses, and highlight the importance of lightweight, non-blocking implementations when maintaining summary structures over highspeed data streams.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116832987","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: 50

Adaptive process management with ADEPT2 使用ADEPT2进行自适应流程管理

21st International Conference on Data Engineering (ICDE'05) Pub Date : 2005-04-05 DOI: 10.1109/ICDE.2005.17

M. Reichert, S. Rinderle-Ma, U. Kreher, P. Dadam

引用次数: 187