다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining - Concepts and Techniques.

A method and system for synchronizing a main database of a server and a local database of a handheld device. A user can use an application residing in the handheld device to make transactions in the local database. During a synchronization operation, the handheld device and server are coupled. The system then determines whether the application should be updated and, if so, causes the server to provide an update. The system also causes the handheld device to provide to the server information related to the transactions made by the user to the local database. The system causes the server to perform transactions on the main database based on the transaction information. The system also causes the server to extract data from the main database. The server can then provide at least some of the extracted data to the handheld device to update the local database.

Method and system for server synchronization with a computing device

A method and system for direct server synchronization (100) with a computing device is disclosed. In one embodiment, the method comprises synchronizing directly a handheld device (110) and an enterprise server (130), which comprises retrieving a record extraction sequence from the server; and extracting records stored on a database according to the record extraction sequence, wherein the extracted records are not already stored on the computing device.

Method and system for direct server synchronization with a computing device

Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.

http://ftp.misq.org/skin/frontend/default/misq/pdf/appendices/2011/BelangerCrosslerAppendices.pdf

Privacy in the digital age: a review of information privacy research in information systems

Deep Web contents are accessed by queries submitted to Web databases and the returned data records are enwrapped in dynamically generated Web pages (they will be called deep Web pages in this paper). Extracting structured data from deep Web pages is a challenging problem due to the underlying intricate structures of such pages. Until now, a large number of techniques have been proposed to address this problem, but all of them have inherent limitations because they are Web-page-programming-language-dependent. As the popular two-dimensional media, the contents on Web pages are always displayed regularly for users to browse. This motivates us to seek a different way for deep Web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep Web pages. In this paper, a novel vision-based approach that is Web-page-programming-language-independent is proposed. This approach primarily utilizes the visual features on the deep Web pages to implement deep Web data extraction, including data record extraction and data item extraction. We also propose a new evaluation measure revision to capture the amount of human effort needed to produce perfect extraction. Our experiments on a large set of Web databases show that the proposed vision-based approach is highly effective for deep Web data extraction.

/pdf/vide-a-vision-based-approach-for-deep-web-data-extraction-53x5o9ntes.pdf

ViDE: A Vision-Based Approach for Deep Web Data Extraction

An increasing number of enterprises outsource their IT services to third parties who can offer these services for a much lower cost due to economy of scale. Quality of service is a major concern in outsourcing. In particular, query integrity, which means that query results returned by the service provider are both correct and complete, must be assured. Previous work requires clients to manage data locally to audit the results sent back by the server, or database engine to be modified for generating authenticated results. In this paper, we introduce a novel integrity audit mechanism that eliminating these costly requirements. In our approach, we insert a small amount of records into an outsourced database so that the integrity of the system can be effectively audited by analyzing the inserted records in the query results. We study both randomized and deterministic approaches for generating the inserted records, as how these records are generated has significant implications for storage and performance. Furthermore, we show that our method is provable secure, which means it can withstand any attacks by an adversary whose computation power is bounded. Our analytical and empirical results demonstrate the effectiveness of our method.

/pdf/integrity-auditing-of-outsourced-data-3uj663rg28.pdf

Integrity auditing of outsourced data

Privacy protection has recently received considerable attention in location-based services. A large number of location cloaking algorithms have been proposed for protecting the location privacy of mobile users. In this paper, we consider the scenario where different location-based query requests are continuously issued by mobile users while they are moving. We show that most of the existing k-anonymity location cloaking algorithms are concerned with snapshot user locations only and cannot effectively prevent location-dependent attacks when users' locations are continuously updated. Therefore, adopting both the location k-anonymity and cloaking granularity as privacy metrics, we propose a new incremental clique-based cloaking algorithm, called ICliqueCloak, to defend against location-dependent attacks. The main idea is to incrementally maintain maximal cliques needed for location cloaking in an undirected graph that takes into consideration the effect of continuous location updates. Thus, a qualified clique can be quickly identified and used to generate the cloaked region when a new request arrives. The efficiency and effectiveness of the proposed ICliqueCloak algorithm are validated by a series of carefully designed experiments. The experimental results also show that the price paid for defending against location-dependent attacks is small.

Protecting Location Privacy against Location-Dependent Attacks in Mobile Services

Privacy preservation has recently received considerable attention for location-based mobile services. In this paper, we present location-dependent attack resulting from continuous and dependent location updates and propose an incremental clique-based cloaking algorithm, called ICliqueCloak, to defend against location-dependent attack. The main idea is to incrementally maintain maximal cliques for location cloaking in an un-directed graph that takes into consideration the effect of continuous location updates.

/pdf/protecting-location-privacy-against-location-dependent-2mjwuszgra.pdf

Protecting location privacy against location-dependent attack in mobile services

Histograms are the workhorse of data mining and analysis. This paper considers the problem of publishing histograms under differential privacy, one of the strongest privacy models. Existing differentially private histogram publication schemes have shown that clustering (or grouping) is a promising idea to improve the accuracy of sanitized histograms. However, none of them fully exploits the benefit of clustering. In this paper, we introduce a new clustering framework. It features a sophisticated evaluation of the trade-off between the approximation error due to clustering and the Laplace error due to Laplace noise injected, which is normally overlooked in prior work. In particular, we propose three clustering strategies with different orders of run-time complexities. We prove the superiority of our approach by theoretical utility comparisons with the competitors. Our extensive experiments over various standard real-life and synthetic datasets confirm that our technique consistently outperforms existing competitors.

https://epubs.siam.org/doi/pdf/10.1137/1.9781611973440.68

Xiaofeng Meng

Papers

ViDE: A Vision-Based Approach for Deep Web Data Extraction

Integrity auditing of outsourced data

Protecting Location Privacy against Location-Dependent Attacks in Mobile Services

Protecting location privacy against location-dependent attack in mobile services

Towards Accurate Histogram Publication under Differential Privacy