K-aggregation: Improving Accuracy for Differential Privacy Synthetic Dataset by Utilizing K-anonymity Algorithm
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