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README.md

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----
-license: cc-by-4.0
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+---
+license: cc-by-4.0
+language:
+- en
+pretty_name: UCI Tabular Benchmark Sample
+task_categories:
+  - tabular-classification
+  - tabular-regression
+tags:
+  - uci
+  - tabular
+  - classification
+  - binary
+  - multiclass
+  - regression
+size_categories:
+  - 10K<n<100K
+  - 100K<n<1M
+---
+
+# UCI Tabular Benchmark Sample
+This repository contains a small collection of tabular datasets mirrored from the UCI Machine Learning Repository and prepared for convenient experimentation.
+
+## Contents
+The datasets are organized by common ML task type:
+
+### Binary Classification
+Adult dataset
+
+Bank Marketing dataset
+
+### Multiclassification
+Covertype dataset
+
+Statlog (Shuttle) dataset
+
+### Regression
+Year Prediction MSD dataset
+
+## Source and License
+
+All datasets in this repository are sourced from the UCI Machine Learning Repository and are used under **Creative Commons Attribution 4.0 International (CC BY 4.0)**.
+
+- CC BY 4.0 license text: https://creativecommons.org/licenses/by/4.0/
+
+### Upstream dataset pages (UCI)
+
+- Adult (Census Income): https://archive.ics.uci.edu/dataset/2/adult
+- Bank Marketing: https://archive.ics.uci.edu/dataset/222/bank+marketing
+- Covertype: https://archive.ics.uci.edu/dataset/31/covertype
+- Statlog (Shuttle): https://archive.ics.uci.edu/dataset/148/statlog+shuttle
+- YearPredictionMSD: https://archive.ics.uci.edu/dataset/203/yearpredictionmsd
+
+## Modifications and Data Processing Notes
+Some modifications may have been applied for usability and consistency. These modifications are intended to be non-substantive and not to change the meaning of the data.
+
+Typical changes include:
+- Adding **column headers** where the original files did not include headers.
+- Converting original formats (for example space-separated or other delimiters) into **`.csv`**.
+- Normalizing line endings and basic formatting fixes to improve parsing.
+- In some cases, reorganizing files into a standard folder structure (for example `train.csv` and `test.csv`).
+
+Unless explicitly stated in a dataset folder README (if present), no attempt was made to:
+- Remove rows or features
+- Alter feature values
+- Rebalance classes
+- Impute missing values
+
+If you require a byte-for-byte identical copy of the upstream distribution, please download directly from the corresponding UCI page.
+
+## Missing Values
+
+Missing values are preserved as in the upstream sources. Depending on the dataset, missingness may appear as empty fields, `?`, or other dataset-specific markers. Refer to each dataset's UCI documentation for the authoritative description.
+
+## Intended Use
+
+This pack is intended for:
+- Tabular model benchmarking (linear models, tree models, neural networks)
+- Privacy and security research on tabular learning pipelines, including:
+  - reconstruction and gradient inversion attacks
+  - defenses such as clipping, noise injection, discretization, constraint-aware decoding
+  - evaluating reconstructibility using feature-level and record-level metrics
+
+It is not intended for making decisions about individuals or for any high-stakes deployment