README.md

---
license: cc-by-nc-sa-4.0
language:
- en
size_categories:
- 1K<n<10K
pretty_name: Android GPU Performance Counter to Key Press Dataset
---

# Android GPU Performance Counter to Key Press Dataset

## Description

This dataset comes from our mobile GPU-based eavesdropping work, [Eavesdropping user credentials via GPU side channels on smartphones](https://doi.org/10.1145/3503222.3507757), presented at the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2022).
It contains 3,466 traces of mapping between the on-screen keyboard key presses and corresponding Snapdragon Adreno GPU performance counter changes collected on device in the meantime.

## Data Structure

The dataset is arranged in the following format:

* Folder name (e.g., `1622457056`): This UNIX timestamp when the experiment took place.
    * `timestamp_data.csv`: Raw recording of GPU performance counter changes during the experiment.
        * Column 1: UNIX timestamp of each performance counter ("PC") value change event, with granularity of 1 microseconds.
        * Column 2-13: GPU PC value changes of different types:
            * `PERF_LRZ_VISIBLE_PRIM_AFTER_LRZ`
            * `PERF_LRZ_FULL_8X8_TILES`
            * `PERF_LRZ_PARTIAL_8X8_TILES`
            * `PERF_LRZ_VISIBLE_PIXEL_AFTER_LRZ`
            * `PERF_RAS_SUPERTILE_ACTIVE_CYCLES`
            * `PERF_RAS_SUPER_TILES`
            * `PERF_RAS_8X4_TILES`
            * `PERF_RAS_FULLY_COVERED_8X4_TILES`
            * `PERF_VPC_PC_PRIMITIVES`
            * `PERF_VPC_SP_COMPONENTS`
            * `PERF_VPC_LRZ_ASSIGN_PRIMITIVES`
            * `PERF_VPC_SP_LM_COMPONENTS`
    * `timestamp_keys.csv`: Keyboard key presses occurred during the experiment.
        * Column 1: UNIX timestamp of each key press, with granularity of 1 microseconds.
        * Column 2: The specific key press occurred.

For the discussion of detailed meanings of different GPU PCs, please refer to Section 4 of [our paper](https://doi.org/10.1145/3503222.3507757).
      
## Citation

If you find this dataset useful, please consider citing the original published paper as shown below:

```
@inproceedings{yang2022eavesdropping,
author = {Yang, Boyuan and Chen, Ruirong and Huang, Kai and Yang, Jun and Gao, Wei},
title = {Eavesdropping user credentials via GPU side channels on smartphones},
year = {2022},
isbn = {9781450392051},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3503222.3507757},
doi = {10.1145/3503222.3507757},
booktitle = {Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems},
pages = {285–299},
numpages = {15},
keywords = {Smartphones, Side Channel, Performance Counters, Mobile GPU, Input Eavesdropping},
location = {Lausanne, Switzerland},
series = {ASPLOS '22}
}
```

## License

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This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].

[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]

[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
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[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg