NDLI: A Practical Differentially Private Method for Publishing Large and Sparse Tabular Data

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A Practical Differentially Private Method for Publishing Large and Sparse Tabular Data

Content Provider	Semantic Scholar
Author	Terada, Masayuki Takeuchi, Daijiro Saito, Katsuya Hongo, Sadayuki
Copyright Year	2014
Abstract	人々に関係するデータベースから作成された集計データを公開するにあたっては,プライバシー保護への十分な配慮が必要となる.本稿では,これら集計データのプライバシーを差分プライバシー (differential privacy) 基準に基づいて保護する上で,データの統計的な正確性と計算効率を改善した,新たなプライバシー保護手法を提案する.なお, 本稿における集計データとは,元のデータベースに含まれる個々のデータ群 1 から作成した,「ある条件を満たす」データの個数を数えあげた数値データ (セル)の集まり 2 を意味し,特に広範囲の空間分布を表す集計データ (たとえば人口分布や交通量分布など) などの大規模で広い属性空間を持つ集計データを主な検討の対象とする. 集計データに対するプライバシー保護の必要性は,統計分野において古くから議論されてきた.これらの統計分野におけるプライバシー保護手法は,統計的開示制御 (statistical disclosure control, SDC) と総称される [9], [10], [11], [20].
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://ipsj.ixsq.nii.ac.jp/ej/index.php?action=pages_view_main&active_action=repository_action_common_download&attribute_id=1&block_id=8&file_no=1&item_id=104903&item_no=1&page_id=13
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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