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Applying Local Differential Privacy for Secure Machine Learning
Local differential privacy
Secure machine learning
|Issue Date:||2019-03-07 12:07:44 (UTC+8)|
訊，個人隱私也隨之面臨洩漏的風險，如何平衡資料的可用性與隱私保護成為重要的課題。本研究運用本地差分隱私技術建構安全式機器學習，在不洩漏個人敏感資訊的情形下完成資料分析的正確分類與預測。本研究使用 UCI 提供的” Bank Marketing Data Set”資料集，運用基於 AnonML 與 RAPPOR 的本地差分隱私技術擾動敏感資料完成隱私保護，允許使用者視特徵隱私程度的不同客製化隱私預算，在三方平台還原資料完成安全式機器學習，並具體提出量化與質化的運算觀察結果。
With the arrival of big data era, many big enterprises and governments aggregate and analyze great amounts of user data. Personal privacy faces the risk of leakage nowadays. It becomes an important task to balance data utility and privacy protection.This research proposed to use local differential privacy to implement secure machine
learning and make correct classification and prediction with the data protection. This research uses the “Bank Marketing Data Set” on UCI, adding noise into sensitive data by local differential privacy based on AnonML and RAPPOR for privacy protection and recover the data to implement machine learning on the third-party platform, and
concluding the calculation results of quantization and quality by this method.
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