K-aggregation: Improving Accuracy for Differential Privacy Synthetic Dataset by Utilizing K-anonymity Algorithm | |
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學年 | 105 |
學期 | 2 |
發表日期 | 2017-03-27 |
作品名稱 | K-aggregation: Improving Accuracy for Differential Privacy Synthetic Dataset by Utilizing K-anonymity Algorithm |
作品名稱(其他語言) | |
著者 | Bo-Chen Tai; Szu-Chuang Li; Yennun Huang |
作品所屬單位 | |
出版者 | |
會議名稱 | AINA 2017 |
會議地點 | Taipei, Taiwan |
摘要 | Enterprises and governments around the world have been attempting to leverage intelligence from the community by making formally in-house database available to the public for analyzing. The released data was often “anonymized”: sensitive attributes were removed from the dataset for privacy protection. However it is proved that masking sensitive attributes alone is not adequate for data protection. Differential privacy can be used to generate “synthetic dataset” that retain statistical properties of the original dataset and limit data-leaking risk at the same time, but there's always a trade-off between data privacy and utility. In this study we aggregate data counts across value with little counts to ease the problem of excessive error at the data value with small data count. Experiments show that K-aggregation has the potential to reduce error of count queries on value with smaller counts. Limitations of this approach are also discussed. |
關鍵字 | differential privacy;synthetic database |
語言 | en |
收錄於 | |
會議性質 | 國際 |
校內研討會地點 | 無 |
研討會時間 | 20170327~20170329 |
通訊作者 | |
國別 | TWN |
公開徵稿 | |
出版型式 | |
出處 | 2017 IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), p.772-779 |
相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/120183 ) |
SDGS | 負責任的消費與生產,和平正義與有力的制度 |