README.md

metadata

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, 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.

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

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.