论文标题
在排他性攻击下的长期数据共享
Long-term Data Sharing under Exclusivity Attacks
论文作者
论文摘要
学习质量通常随数据的规模和多样性而提高。因此，公司和机构可以从建立模型上受益于共享数据。许多云和区块链平台以及政府计划都有兴趣提供此类服务。 这些合作的努力面临着一个挑战，我们将其称为``排他性攻击''。公司可以共享扭曲的数据，以便学习最佳模型拟合度，但也能够误导他人。我们研究了长期互动的协议及其对这些攻击的脆弱性，尤其是回归和聚类任务。我们得出的结论是，协议的选择以及攻击者可能控制的SYBIL身份的数量是脆弱性的重要性。
The quality of learning generally improves with the scale and diversity of data. Companies and institutions can therefore benefit from building models over shared data. Many cloud and blockchain platforms, as well as government initiatives, are interested in providing this type of service. These cooperative efforts face a challenge, which we call ``exclusivity attacks''. A firm can share distorted data, so that it learns the best model fit, but is also able to mislead others. We study protocols for long-term interactions and their vulnerability to these attacks, in particular for regression and clustering tasks. We conclude that the choice of protocol, as well as the number of Sybil identities an attacker may control, is material to vulnerability.
