An increasing amount of information today is generated, exchanged, and stored digitally. This also includes long-lived and highly sensitive information (e.g., electronic health records, governmental documents) whose integrity and confidentiality must be protected over decades or even centuries. While there is a vast amount of cryptography-based data protection schemes, only few are designed for long-term protection. Recently, Braun et al. (AsiaCCS’17) proposed the first long-term protection scheme that provides renewable integrity protection and information-theoretic confidentiality protection. However, computation and storage costs of their scheme increase significantly with the number of stored data items. As a result, their scheme appears suitable only for protecting databases with a small number of relatively large data items, but unsuitable for databases that hold a large number of relatively small data items (e.g., medical record databases).In this work, we present a solution for efficient long-term integrity and confidentiality protection of large datasets consisting of relatively small data items. First, we construct a renewable vector commitment scheme that is information-theoretically hiding under selective decommitment. We then combine this scheme with renewable timestamps and information-theoretically secure secret sharing. The resulting solution requires only a single timestamp for protecting a dataset while the state of the art requires a number of timestamps linear in the number of data items. Furthermore, we extend the scheme, that supports a single client, to a multi-client setting. Subsequently, we characterize the arising challenges with respect to integrity and confidentiality and discuss how our multi-client scheme tackles them. We implemented our solution and measured its performance in a scenario where 9600 data items are aggregated, stored, protected, and verified over a time span of 80 years. Our measurements show that our new solution completes this evaluation scenario an order of magnitude faster than the state of the art.

中文翻译：

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ELSA：大型数据集的有效长期安全存储（完整版）^*

如今，越来越多的信息以数字方式生成，交换和存储。这还包括长期存在且高度敏感的信息（例如电子健康记录，政府文件），其完整性和机密性必须在数十年甚至几个世纪内得到保护。尽管有大量的基于密码术的数据保护方案，但只有很少几种可以进行长期保护。最近，Braun等人。（AsiaCCS'17）提出了第一个长期保护计划，该计划提供了可再生的完整性保护和信息论的机密性保护。但是，其方案的计算和存储成本随着所存储数据项的数量而显着增加。结果，他们的方案似乎仅适用于保护具有少量相对较大数据项的数据库，但是不适用于包含大量相对较小数据项的数据库（例如病历数据库）。在这项工作中，我们提出了一种对包含相对较小数据项的大型数据集进行有效的长期完整性和机密性保护的解决方案。首先，我们构建了一个可再生的矢量承诺方案，该方案在信息理论上隐藏在选择性授权下。然后，我们将此方案与可更新时间戳和信息理论上安全的秘密共享相结合。最终的解决方案仅需要单个时间戳来保护数据集，而现有技术需要大量与数据项成线性关系的时间戳。此外，我们将支持单个客户端的方案扩展到了多客户端设置。后来，我们将就完整性和机密性方面出现的挑战进行描述，并讨论我们的多客户计划如何应对这些挑战。我们实施了该解决方案，并在80年的时间范围内对9600个数据项进行聚合，存储，保护和验证的情况下，对其性能进行了评估。我们的测量结果表明，我们的新解决方案以比现有技术快一个数量级的速度完成了此评估方案。