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| Title: | 大數據經濟發展下企業利用個資方式所面臨的個資法規範分析—以金控公司建立客戶信用分數的剖析模型（Profiling）專案為例
Personal Data Protection in the Era of Big Data — Case Study of a Taiwanese Financial Holding Company’s Customer Profiling Practice |
| Authors: | 鄭依明
Cheng, Yi-Ming |
| Contributors: | 鄭菀瓊
Cheng, Wan-Chiung
鄭依明
Cheng, Yi-Ming |
| Keywords: | 人工智慧
數據分析
個資法
個資管理
隱私衝擊評估
個案研究
剖析模型
大數據經濟
AI(artificial intelligence)
Data analysis
Personal data protection
Personal data management
Privacy risk assessment
Case study
Profiling model
Big data |
| Date: | 2019 |
| Issue Date: | 2019-08-07 17:05:05 (UTC+8) |
| Abstract: | 隨著全球大數據經濟不斷的發展，企業利用手邊數據進行新商業機會開發的行為，逐漸成為受到普遍重視的新商業模式發展策略。尤其是對於面臨著產業轉型需求與傳統產業升級壓力的台灣企業來說，這些企業正嘗試跟隨這波浪潮，不斷的投入公司資源進行數據的利用與研究，以優化舊有服務與打造新服務為目的，盡可能尋找各種能為公司創造價值的數據利用模式。其中，利用大量的個人資料建立客戶剖析模型以創造新商業價值的模式，更是許多企業在近年來成功利用於消費者分析、精準行銷與客製化服務設計等領域之典範，而被視為擁有極大發展潛力的個人資料利用方式。
但企業所追求極大化個人資料價值的利用目的，與個資法中資料隱私權給予資料擁有者控制個人資料的保護目的，兩者是相互矛盾的。針對企業應該如何平衡該矛盾的議題探討，雖然目前相關討論的文獻漸多，但大多數僅以理論面的探討為主，而少有企業內部實際運作的討論，因而難以得知企業實際面對此議題所產生的情境為何。
因此，本文提供了一實際企業進行剖析模型開發案例的介紹，並整理個資法關於個資保護範圍、個資管理模式的現有規範，嘗試分析出企業在個資法的規範下所面臨的問題與困難，並給予未來要利用個人資料進行剖析模型建立的企業一個完整的參考依據。而本研究將藉由與台灣知名金控公司的大數據團隊管理者進行深度訪談，獲取剖析模型建立的實際資料處理情境，並同時進行關於個資保護範圍、企業個資管理模式的文獻探討，最後也會包含結合理論面與實務面綜合分析。
本研究結果發現，由於建立剖析模型的資料處理情境十分複雜且擁有高流程變動性，同時加上有人工智慧特性的演算法參與使得資料處理結果難以預期，造成了企業高昂的管理成本與在資料管理上的限制。而這樣的情形將導致企業在剖析模型建立的過程中容易忽略了對於個人資料的保護，產生許多潛在的隱私侵害來源。因此，企業應在過程中進行隱私衝擊評估，並密切關注隱私衝擊來源的轉換，建立有效率的隱私風險管理模式，以平衡在大數據時代下利用個資行為與個資法保護目的之矛盾。
With the continuous growth in global big data market, it becomes more and more essential for companies to develop their own strategy so as to find new business opportunities by exploiting data. Especially for companies in Taiwan which face the desperate need of transformation in conventional industries and industries upgrading, they are trying to follow up the overwhelming trend of big data by constantly investing resources in exploiting data and related research.
Recently, the method of using lots of personal data to build customer profiling models has been considered as one of the most promising ways to exploit personal data. However, it may somehow cause conflicts between the goal that companies pursue to maximize the value of personal data and the core value that personal data protection law provides people the right to control their own personal data. In spite of the fact that there are more and more researches about the issue of how to resolve the contradiction, most of them were mainly focus on theoretical discussion and yet lack of providing practical cases inside the company. Therefore, the real impact for company in dealing with this issue is still not unveiled.
Accordingly, this paper provides an actual profiling model building scenario to discuss this problem. In addition, it sorts out the regulations of the personal data protection law, trying to figure out what kind of the data should be protected and how should the company manage them, providing comprehensive suggestions for companies which are going to build profiling model with personal data in the future.
To sum up, the process of building profiling model is so sophisticated and fluctuated as well as the implementation of AI Algorithm which makes output even more unpredictable, the company are now facing heavy managerial cost and the limitation of data management. That leads to the ignorance and reluctance of personal data protection for company; meanwhile, it may also cause lots of potential privacy violations. Therefore, the company should keep an eye on the possible sources of the privacy risks from time to time and establish the adapting risk control system effectively. |
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| Description: | 碩士
國立政治大學
科技管理與智慧財產研究所
105364210 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G1053642101 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU201900050 |
| Appears in Collections: | [科技管理與智慧財產研究所] 學位論文
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