  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. We
implemented our solution and measured its performance in a scenario where 12
000 data items are aggregated, stored, protected, and verified over a time span
of 100 years. Our measurements show that our new solution completes this
evaluation scenario an order of magnitude faster than the state of the art.
