國立高雄大學圖資館 |

Language: English

Back

適用於自發性通報系統資料公開的隱私保護技術 = Privacy Pres...

國立高雄大學資訊工程學系碩士班

適用於自發性通報系統資料公開的隱私保護技術 = Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data

Record Type:	Language materials, printed : monographic
Paralel Title:	Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data
Author:	楊敦筌,
Secondary Intellectual Responsibility:	國立高雄大學
Place of Publication:	[高雄市]
Published:	撰者;
Year of Publication:	2015[民104]
Description:	85面圖，表 : 30公分;
Subject:	藥物不良反應
Subject:	adverse drug reaction
Online resource:	http://handle.ncl.edu.tw/11296/ndltd/82908416283220045914
Notes:	105年10月25日公開
Notes:	參考書目：面69-72
Summary:	近年來，大部分的先進國家都設立了自發性通報系統(SRS)，用於藥物不良反應的偵測與分析，例如，美國食品藥物管制局的不良反應事件通報系統(FAERS)。通常SRS資料會包含敏感的個人健康資訊，應加以保護，以防止個人的隱私外洩。因此我們在公開資料之前，必須將其進行隱匿處理，此技術稱為公開資料的隱私保護技術 (PPDP)。雖然在已有許多學者對於PPDP 進行了很多研究，但很少研究側重於保護SRS 資料的隱私。在本論文中，我們提出了一些SRS資料的特性，主要包含四點：稀有事件，多重個人記錄，多值的敏感屬性與缺漏值。我們並研究了許多當代的隱私保護模型，發現若將它們應用在 SRS 資料集，無法完全處理這些問題，也提醒我們開發新的隱私保護模型與相關演算法是必要的。 In recent years, spontaneous reporting systems (SRSs) have been widely established to collect adverse drug events (ADEs) for ADR detection and analysis, e.g., the FDA Adverse Event Reporting System (FAERS). Usually, SRS data contain sensitive personal health information that should be protected to prevent the identification of individuals, raising the need of anonymizing the raw data before being published, namely privacy-preserving data publishing (PPDP). Although much work has been done on PPDP, very few studies have focused on protecting privacy of SRS data. In this thesis, we present the problem of and research issues for anonymizing spontaneous ADE reporting data for privacy-preserving ADR signal detection first. Four main characteristics of spontaneous ADE data are identified, including rare ADE events, multiple individual records, multi-valued sensitive attribute, and missing values. We examine the feasibility of contemporary privacy-preserving models for anonymizing SRS datasets, showing their incompetence in handling these issues and so arouse the need of new privacy models and data anonymizing methods.

適用於自發性通報系統資料公開的隱私保護技術 = Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data
楊, 敦筌

適用於自發性通報系統資料公開的隱私保護技術 = Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data / 楊敦筌撰 - [高雄市] : 撰者, 2015[民104]. - 85面 ; 圖，表 ; 30公分.
105年10月25日公開參考書目：面69-72.
藥物不良反應adverse drug reaction

適用於自發性通報系統資料公開的隱私保護技術 = Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data
LDR:03277nam0a2200277 450 001 458393
005 20161117141504.0
010 0 $b 精裝
010 0 $b 平裝
100 $a 20151007d2015 k y0chiy50 b
101 1 $a eng $d chi $d eng
102 $a tw
105 $a ak am 000yy
200 1 $a 適用於自發性通報系統資料公開的隱私保護技術 $d Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data $z eng $f 楊敦筌撰
210 $a [高雄市] $c 撰者 $d 2015[民104]
215 0 $a 85面 $c 圖，表 $d 30公分
300 $a 105年10月25日公開
300 $a 參考書目：面69-72
314 $a 指導教授：林文揚博士
328 $a 碩士論文--國立高雄大學資訊工程學系碩士班
330 $a 近年來，大部分的先進國家都設立了自發性通報系統(SRS)，用於藥物不良反應的偵測與分析，例如，美國食品藥物管制局的不良反應事件通報系統(FAERS)。通常SRS資料會包含敏感的個人健康資訊，應加以保護，以防止個人的隱私外洩。因此我們在公開資料之前，必須將其進行隱匿處理，此技術稱為公開資料的隱私保護技術 (PPDP)。雖然在已有許多學者對於PPDP 進行了很多研究，但很少研究側重於保護SRS 資料的隱私。在本論文中，我們提出了一些SRS資料的特性，主要包含四點：稀有事件，多重個人記錄，多值的敏感屬性與缺漏值。我們並研究了許多當代的隱私保護模型，發現若將它們應用在 SRS 資料集，無法完全處理這些問題，也提醒我們開發新的隱私保護模型與相關演算法是必要的。 In recent years, spontaneous reporting systems (SRSs) have been widely established to collect adverse drug events (ADEs) for ADR detection and analysis, e.g., the FDA Adverse Event Reporting System (FAERS). Usually, SRS data contain sensitive personal health information that should be protected to prevent the identification of individuals, raising the need of anonymizing the raw data before being published, namely privacy-preserving data publishing (PPDP). Although much work has been done on PPDP, very few studies have focused on protecting privacy of SRS data. In this thesis, we present the problem of and research issues for anonymizing spontaneous ADE reporting data for privacy-preserving ADR signal detection first. Four main characteristics of spontaneous ADE data are identified, including rare ADE events, multiple individual records, multi-valued sensitive attribute, and missing values. We examine the feasibility of contemporary privacy-preserving models for anonymizing SRS datasets, showing their incompetence in handling these issues and so arouse the need of new privacy models and data anonymizing methods.
510 1 $a Privacy Preserving Data Publishing Techniques for Spontaneous Reporting System Data $z eng
610 # 0 $a 藥物不良反應 $a 資料隱匿 $a 公開資料的隱私保護技術 $a 自發性通報系統
610 # 1 $a adverse drug reaction $a data anonymization $a privacy preserving data publishing $a spontaneous reporting system
681 $a 008M/0019 $b 464103 4608 $v 2007年版
700 1 $a 楊 $b 敦筌 $4 撰 $3 709637
712 0 2 $a 國立高雄大學 $b 資訊工程學系碩士班 $3 353878
801 0 $a tw $b NUK $c 20161014 $g CCR
856 7 # $u http://handle.ncl.edu.tw/11296/ndltd/82908416283220045914 $2 http $z PDF全文