Prior research primarily examined differentially-private continual releases against data streams, where entries were immutable after insertion. However, most data is dynamic and housed in databases. Addressing this literature gap, this article presents a methodology for achieving differential privacy for continual releases in dynamic databases, where entries can be inserted, modified, and deleted. A dynamic database is represented as a changelog, allowing the application of differential privacy techniques for data streams to dynamic databases. To ensure differential privacy in continual releases, this article demonstrates the necessity of constraints on mutations in dynamic databases and proposes two common constraints. Additionally, it explores the differential privacy of two fundamental types of continual releases: Disjoint Continual Releases (DCR) and Sliding-window Continual Releases (SWCR). The article also highlights how DCR and SWCR can benefit from a hierarchical algorithm for better privacy budget utilization. Furthermore, it reveals that the changelog representation can be extended to dynamic entries, achieving local differential privacy for continual releases. Lastly, the article introduces a novel approach to implement continual release of randomized responses.

翻译：现有研究主要探讨了针对数据流的差分隐私持续发布，其中数据条目在插入后不可更改。然而，大多数数据是动态的，存储于数据库中。为填补这一研究空白，本文提出了一种在动态数据库中实现持续发布差分隐私的方法，其中条目可被插入、修改和删除。动态数据库以变更日志表示，从而能够将针对数据流的差分隐私技术应用于动态数据库。为确保持续发布中的差分隐私，本文论证了动态数据库中突变约束的必要性，并提出了两种常见的约束条件。此外，本文探讨了两种基本持续发布类型——不相交持续发布（DCR）和滑动窗口持续发布（SWCR）——的差分隐私特性。本文还阐述了DCR和SWCR如何通过分层算法优化隐私预算利用率。更进一步，本文揭示了变更日志表示可扩展至动态条目，从而实现持续发布的局部差分隐私。最后，本文提出了一种实现随机化响应持续发布的新方法。