标题： SKALD：大规模数据集的可扩展K匿名化 显示英文标题

标题： SKALD: Scalable K-Anonymisation for Large Datasets

摘要： 数据隐私和匿名化是当今数据驱动社会中的关键问题，特别是在处理个人和敏感用户数据时。全球范围内的监管框架推荐诸如k-匿名化等隐私保护协议来去除表格数据发布的身份标识。 可用的硬件资源为每次可以处理的最大数据集规模提供了上限。 超过此上限的大数据集必须拆分成较小的数据块进行处理。 在这种情况下，标准的k-匿名化工具（如ARX）只能逐块操作。 本文提出了一种名为SKALD的新算法，用于在有限RAM的情况下对大规模数据集进行k-匿名化。 我们的SKALD算法通过在处理过程中从每个数据块中提取和组合足够的统计信息，提供多倍于标准k-匿名化方法的性能改进，以确保成功实现k-匿名化并提供更好的实用性。 显示英文摘要

摘要： Data privacy and anonymisation are critical concerns in today's data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide recommend privacy-preserving protocols such as k-anonymisation to de-identify releases of tabular data. Available hardware resources provide an upper bound on the maximum size of dataset that can be processed at a time. Large datasets with sizes exceeding this upper bound must be broken up into smaller data chunks for processing. In these cases, standard k-anonymisation tools such as ARX can only operate on a per-chunk basis. This paper proposes SKALD, a novel algorithm for performing k-anonymisation on large datasets with limited RAM. Our SKALD algorithm offers multi-fold performance improvement over standard k-anonymisation methods by extracting and combining sufficient statistics from each chunk during processing to ensure successful k-anonymisation while providing better utility.