论文标题
在隐私限制下在拍卖中出售数据
Selling Data at an Auction under Privacy Constraints
论文作者
论文摘要
私人数据查询将机制设计与隐私保护相结合，以从私有数据记录中产生汇总的统计数据。该问题出现在数据市场中，数据所有者具有个性化的隐私要求和私人数据估值。我们专注于数据所有者一心一意的情况，即，只有在数据经纪人保证满足其宣布的隐私要求的情况下，他们才愿意发布数据。对于想要从此类数据所有者那里购买数据的数据经纪人，我们提出了SinglembonedQuery（SMQ）机制，该机制使用反向拍卖来选择数据所有者并确定补偿。 SMQ满足临时激励兼容性，个人合理性和预算可行性。此外，它使用购买的隐私期望最大化作为原理，以为常用查询（例如计数，中值和线性预测指标）产生准确的输出。我们方法的有效性通过一系列实验在经验上得到了证实。
Private data query combines mechanism design with privacy protection to produce aggregated statistics from privately-owned data records. The problem arises in a data marketplace where data owners have personalised privacy requirements and private data valuations. We focus on the case when the data owners are single-minded, i.e., they are willing to release their data only if the data broker guarantees to meet their announced privacy requirements. For a data broker who wants to purchase data from such data owners, we propose the SingleMindedQuery (SMQ) mechanism, which uses a reverse auction to select data owners and determine compensations. SMQ satisfies interim incentive compatibility, individual rationality, and budget feasibility. Moreover, it uses purchased privacy expectation maximisation as a principle to produce accurate outputs for commonly-used queries such as counting, median and linear predictor. The effectiveness of our method is empirically validated by a series of experiments.
