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Block-Sphere Vector Quantization GroupAffect-4: A Multimodal Dataset of Four-Person Collaborative Interaction CogScale: Scalable Benchmark for Sequence Processing TextAlign: Preference Alignment for Text Rendering with Hierarchical Rewards LogRouter: Adaptive Two-Level LLM Routing for Log Question Answering in Big Data Systems Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics Covariance Structure and Coordinate Heterogeneity Govern Binary Quantization of Contrastive Embeddings IVF-TQ: Calibration-Free Streaming Vector Search via a Codebook-Free Residual Layer Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation Towards Foundation Models for Relational Databases with Language Models and Graph Neural Networks Gaussian Relational Graph Transformer Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets Reducing Hallucination in Vision-Language Models via Stage-wise Preference Optimization under Distribution Shift A Horn extension of DL-Lite with NL data complexity 3D Primitives are a Spatial Language for VLMs Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance A CAP-like Trilemma for Large Language Models: Correctness, Non-bias, and Utility under Semantic Underdetermination EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models Toward Multi-Database Query Reasoning for Text2Cypher Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities Detect, Localize, and Explain: Interactive Hierarchical Log Anomaly Analytics with LLM Augmentation Open Ontologies: Tool-Augmented Ontology Engineering with Stable Matching Alignment Machine Learning-Based Pre-Test Risk Stratification for PCR-Confirmed Chlamydia Using Patient-Reported Data and Urine Biomarkers Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations PrepBench: How Far Are We from Natural-Language-Driven Data Preparation? Anatomy of a Query: W5H Dimensions and FAR Patterns for Text-to-SQL Evaluation Building informative materials datasets beyond targeted objectives
SPSW: Database Watermarking Based on Fake Tuples and Spar...
Zhiwen Ren, Zehua Ma, Weiming Zhang, Nenghai Yu · 2024-05-08 · via cs.DB updates on arXiv.org

Databases play a crucial role in storing and managing vast amounts of data in various organizations and industries. Yet the risk of database leakage poses a significant threat to data privacy and security. To trace the source of database leakage, researchers have proposed many database watermarking schemes. Among them, fake-tuples-based database watermarking shows great potential as it does not modify the original data of the database, ensuring the seamless usability of the watermarked database. However, the existing fake-tuple-based database watermarking schemes need to insert a large number of fake tuples for the embedding of each watermark bit, resulting in low watermark transparency. Therefore, we propose a novel database watermarking scheme based on fake tuples and sparse priority strategy, named SPSW, which achieves the same watermark capacity with a lower number of inserted fake tuples compared to the existing embedding strategy. Specifically, for a database about to be watermarked, we prioritize embedding the sparsest watermark sequence, i.e., the sequence containing the most `0' bits among the currently available watermark sequences. For each bit in the sparse watermark sequence, when it is set to `1', SPSW will embed the corresponding set of fake tuples into the database. Otherwise, no modifications will be made to the database. Through theoretical analysis, the proposed sparse priority strategy not only improves transparency but also enhances the robustness of the watermark. The comparative experimental results with other database watermarking schemes further validate the superior performance of the proposed SPSW, aligning with the theoretical analysis.