Information Processing on Compressed Data

Abstract

We survey our recent work related to information processing on compressed strings. Note that a “string” here contains any fixed-length sequence of symbols and therefore includes not only ordinary text but also a wide range of data, such as pixel sequences and time-series data. Over the past two decades, a variety of algorithms and their applications have been proposed for compressed information processing. In this survey, we mainly focus on two problems: recompression and privacy-preserving computation over compressed strings. Recompression is a framework in which algorithms transform a given compressed data into another compressed format without decompression. Recent studies have shown that a higher compression ratio can be achieved at lower cost by using an appropriate recompression algorithm such as preprocessing. Furthermore, various privacy-preserving computation models have been proposed for information retrieval, similarity computation, and pattern mining.