The Effects of Nudging a Privacy Setting Suggestion Algorithm's Outputs on User Acceptability

• Nakamura Toru

  Advanced Telecommunications Research Institute International

• Adams Andrew A.

  Meiji University

• Murata Kiyoshi

  Meiji University

• Kiyomoto Shinsaku

  KDDI Research, Inc.

• Suzuki Nobuo

  Advanced Telecommunications Research Institute International Kindai University

抄録

In prior work, a machine learning approach was used to develop a suggestion system for 80 privacy settings, based on a limited sample of five user preferences. Such suggestion systems may help with the user-burden of preference selection. However, such a system may also be used by a malicious provider to manipulate users' preference selections through nudging the output of the algorithm. This paper reports an experiment with such manipulation to clarify the impact and users' resistance of or susceptibility to such manipulation. Users are shown to be highly accepting of suggestions, even where the suggestions are random (though less so than for nudged suggestions).

収録刊行物

• Journal of Information Processing

  Journal of Information Processing 27 (0), 787-801, 2019

  一般社団法人 情報処理学会

キーワード

• privacy protection
• machine learning
• recommendation
• nudge effect

詳細情報

• CRID: 1390565134805010816
• NII論文ID: 130007762297
• DOI: 10.2197/ipsjjip.27.787
• ISSN: 18826652
• Web Site: https://www.jstage.jst.go.jp/article/ipsjjip/27/0/27_787/_pdf
• 本文言語コード: en
• データソース種別:
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