解决个性化差分隐私下的数据效用失衡问题
INO-SGD: Addressing Utility Imbalance under Individualized Differential Privacy
- 通过动态降低敏感数据权重，均衡模型对高隐私需求数据的拟合
- 在满足个性化差分隐私的前提下，提升敏感数据上的模型性能
- 适合关注隐私保护与模型公平性的机器学习研究者
差分隐私（DP）广泛用于机器学习中保护训练数据的机密性。随着个人数据所有权意识增强，数据所有者更倾向于设定个性化的隐私要求，这催生了个体化差分隐私（IDP）。尤其对于敏感数据（如被污名化疾病的阳性病例），其所有者通常要求更强的隐私保护，因数据泄露可能带来严重社会影响。然而，现有IDP算法存在关键的效用失衡问题：隐私要求更高的数据在训练中被严重低估，导致部署时对类似数据的预测性能下降。本文分析该问题并提出INO-SGD算法，在每个批次中策略性地降低数据权重，以改善所有迭代中高隐私数据的表现。值得注意的是，该算法专为满足IDP设计，而现有缓解效用失衡的方法既不满足IDP，也难以适配。最后，我们验证了该方法的实证可行性。
原文摘要 · Abstract (English)
Differential privacy (DP) is widely employed in machine learning to protect confidential or sensitive training data from being revealed. As data owners gain greater control over their data due to personal data ownership, they are more likely to set their own privacy requirements, necessitating individualized DP (IDP) to fulfil such requests. In particular, owners of data from more sensitive subsets, such as positive cases of stigmatized diseases, likely set stronger privacy requirements, as leakage of such data could incur more serious societal impact. However, existing IDP algorithms induce a critical utility imbalance problem: Data from owners with stronger privacy requirements may be severely underrepresented in the trained model, resulting in poorer performance on similar data from subsequent users during deployment. In this paper, we analyze this problem and propose the INO-SGD algorithm, which strategically down-weights data within each batch to improve performance on the more private data across all iterations. Notably, our algorithm is specially designed to satisfy IDP, while existing techniques addressing utility imbalance neither satisfy IDP nor can be easily adapted to do so. Lastly, we demonstrate the empirical feasibility of our approach.
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