Datasets:

jamesflemings
/

PrivacySIM

license: cc-by-4.0
language:
  - en
tags:
  - privacy
  - llm-evaluation
  - llm-simulation
  - alignment
size_categories:
  - 1K<n<10K
configs:
  - config_name: full_dataset
    data_files:
      - split: all
        path: full_dataset.csv
  - config_name: sampled
    data_files:
      - split: all
        path: dataset.csv

PrivacySIM: An Evaluation Suite for LLM Simulation of User Privacy Behavior

PrivacySIM aggregates user privacy preferences from five published user studies into a unified evaluation suite for LLM simulation of user privacy behavior. Each row represents one participant's responses to a study-specific questionnaire about whether data-sharing involving an LLM is appropriate, plus persona facets (demographics, previous experience, privacy attitudes).

The five contributing studies cover distinct data-sharing contexts: LLM chatbots, LLM healthcare consultations, AI agent permissions, group chat with bots, and conversational agents. Identifiers are derived deterministically from (user_id, domain) via UUID5.

Files

File	Rows	Purpose
`full_dataset.csv`	2,000	All users from all five studies, no filtering or sampling.
`dataset.csv`	1,000	Sampled to 200 users per domain (healthcare filtered to records with informative previous-experience values), `random_state=42`.

Schema notes

Several columns (demographics, previous_experiences, privacy_attitudes, responses, preferences) hold dict-shaped values. Four of the five contributing studies stored these as Python dict reprs (single-quoted); the fifth uses JSON. Both forms parse with ast.literal_eval:

import ast
import pandas as pd

df = pd.read_csv("dataset.csv")
prefs = ast.literal_eval(df.iloc[0]["preferences"])
# -> {question_text: user_response, ...}

A column-by-column schema for every file is documented at docs/SCHEMA.md in the accompanying code repository.

Loading

from datasets import load_dataset

# Full unfiltered dataset (2,000 users)
ds = load_dataset("jamesflemings/PrivacySIM", "full_dataset", split="all")

# 1,000-user sampled from paper
ds = load_dataset("jamesflemings/PrivacySIM", "sampled", split="all")

Source studies

The data is derived from five public user studies. Each row's domain column identifies the source study. Please cite the original studies in addition to this dataset if you use the corresponding rows.

`domain`	Source	Users in `full_dataset.csv`	Users in `dataset.csv`
`LLM healthcare consultation`	LLM health consultation user study	846	200
`Chatbot group chat`	Bot-among-us chatbot group chat study	374	200
`LLM chatbot`	LLM chatbot privacy norms factorial vignette study	300	200
`LLM conversational agents`	LLM conversational-agent privacy preferences study	277	200
`AI agent permissions`	AI agent permission user study	203	200
Total		2,000	1,000

Code

The full reproducibility pipeline (data download, processing, simulation, evaluation, plotting) is at github.com/james-flemings/PrivacySIM. See docs/SCHEMA.md for the column-by-column reference.

License

The combined dataset is released under Creative Commons Attribution 4.0 International (CC BY 4.0). Per-row content remains under each source study's original license:

`domain`	Source license
`LLM healthcare consultation`	MIT
`Chatbot group chat`	MIT
`LLM chatbot`	CC0 1.0 (public-domain dedication)
`AI agent permissions`	CC BY 4.0
`LLM conversational agents`	CC BY 4.0

Full attribution, license texts, copyright notices, and indication of modifications for every source study are provided in the THIRD_PARTY_LICENSES.md file shipped alongside the dataset. Anyone redistributing or building on this dataset must preserve that attribution file (or an equivalent).