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Data engineers invest significant effort in the early stages of data analysis, including identifying relevant datasets in large and complex data lakes. Data retrieval efficiency becomes an urgent concern as data volumes continue to increase with structures that vary in size and complexity. This paper presents a novel strategy for accelerating data discovery in data lakes. Our approach integrates self-supervised techniques and weighted similarity estimation for efficient dataset classification, facilitating faster search and retrieval. By extracting meta-feature characteristics, our approach improves data traceability in data lakes through clustering, resulting in significant improvements in modularity, showing an efficiency gain of 69.8%. Regarding dataset search through classification, it consistently achieves AUC-ROC scores exceeding 0.85, indicating strong performance in class differentiation. Finally, our method reduces the overall execution time more than twofold and shows promising applications for addressing real-world challenges in various data lake domains. [ABSTRACT FROM AUTHOR]

Text similarity measures have become increasingly pivotal in text-related research and applications, encompassing tasks such as information retrieval, text classification, document clustering. Unveiling the equality relationship between encrypted words is fundamental for ensuring privacy-preserving text similarity. Identity-based encryption with equality test (IBEET) emerges as a promising solution for computing similarity over encrypted data in cloud environment. However, prevalent IBEET schemes often lack control over the lifespan of trapdoors, potentially leading to misuse and unauthorized disclosure of users' privacy. In this paper, we introduce a novel primitive known as controllable forward secure identity-based encryption with equality test (CFS-IBEET), designed to support both permanent trapdoor and temporary trapdoor. We present a concrete CFS-IBEET scheme based on bilinear pairing and conduct a comprehensive security analysis against two types of adversaries in random oracle model. The comprehensive performance evaluation shows that our CFS-IBEET scheme offers significant advantages of high efficiency of encryption, decryption, trapdoor generation and test algorithms as well as the guarantee of controllable forward security, showcasing its applicability for collaborative filtering recommendation system in E-commerce age. • Propose a stronger IBEET for privacy-preserving text similarity. • Flexibly control the granularity of time-based authorization for forward security. • Formally prove its security against two adversaries in random oracle model. • Show its practical application in collaborative filtering recommendation system thanks to its significant high efficiency. [ABSTRACT FROM AUTHOR]