As personal data has been the new oil of the digital era, there is a growing trend perceiving personal data as a commodity. Existing studies have built theories on how to map the privacy loss to an arbitrage-free price. They assumed that a data buyer could purchase arbitrarily accurate results as long as she could compensate data owners for their privacy loss. However, it may not be a viable business model under strict privacy regulations, such as GDPR and CCPA, and data owners’ emerging privacy concerns. In this paper, we study how to empower data owners with the control of privacy loss when continuously trading their personal mobile data. Concretely, we propose a framework for trading infinite streaming mobile data which enables each data owner to bound her privacy loss in a w-length sliding window. Introducing such upper bounds of privacy loss makes the existing trading frameworks invalid and raises new technical challenges in terms of budget allocation and arbitrage-free pricing. To address these problems, we propose a modularized trading framework with instances that allows data owners to personalize their privacy loss while the price is still arbitrage-free. Finally, we conduct experiments to verify the effectiveness of the proposed trading protocols.
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| 氏名 | 専攻 | 研究室 | 役職/学年 |
|---|---|---|---|
| Shuyuan Zheng | 社会情報学専攻 | 吉川・馬研究室 | 博士1回生 |
| Yang Cao | 社会情報学専攻 | 吉川・馬研究室 | 特定助教 |
| Masatoshi Yoshikawa | 社会情報学専攻 | 吉川・馬研究室 | 教授 |