PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action

| Paper | HF Dataset | Website |

Overview

PrivacyLens is a data construction and multi-level evaluation framework for evaluating privacy norm awareness of language models in action.

What you can do with PrivacyLens?

1. Constructing contextualized data points.

PrivacyLens proposes to uncover privacy-sensitive scenarios with three levels of data points:

• Privacy-Sensitive Seed: A negative privacy norm expressed as a 5-tuple, (data type, data subject, data sender, data recipient, transmission principle).
• Vignette: An expressive piece of story that captures the essence of a privacy-sensitive seed.
• Trajectory: A sequence of LM agent actions and the environment observations that simulate the behavior of an agent in a privacy-sensitive scenario, excluding the final action.

The effort required to obtain data points increases from seed to vignette to trajectory. To help create more contextualized data points, PrivacyLens offers a data construction pipeline that programmatically converts seeds into vignettes and trajectories.

2. Conducting both probing-based and action-based evaluations.

• Probing-based Evaluation: PrivacyLens provides multi-choice question template to probe the privacy norm awareness of LMs at different levels.
• Action-based Evaluation: PrivacyLens evaluates the final action of an LM agent using the trajectory data points and computes the leakage rate and helpfulness rating of the final action.

Setup

1. Clone the git repository.

   git clone https://github.com/SALT-NLP/PrivacyLens.git
   cd PrivacyLens

2. Install the required packages.

   conda create -n privacylens python=3.11
   conda activate privacylens
   pip install -r requirements.txt

3. Set up API keys. Create a .env file in the root directory and add the following (if you do not need to use GPT or Claude, you can skip this step):

   OPENAI_API_TYPE={azure/openai}
   OPENAI_API_VERSION={api_version, only used for azure service}
   OPENAI_API_BASE={api_base, only used for azure service}
   OPENAI_API_KEY={openai_api_key}
   ANTHROPIC_API_KEY={anthropic_api_key}
   

Quick Start

Before delving into the details of replicating our study or running experiment scripts, we provide a quick-start notebook to walk you through what PrivacyLens can do.

Evaluate LMs' Privacy Norm Awareness

Dataset

In the paper, we collect 493 privacy-sensitive seeds from U.S. privacy regulations, privacy literature on vulnerable groups, and crowdsourcing. We further extend them into vignettes and trajectories.

The dataset is available in data/main_data.json, and each datapoint has the following structure:

{
   "name": "unique id",
   "seed": {
      "data_type": "data type",
      "data_subject": "who the data is about",
      "data_sender": "who sends the data",
      "data_sender_name": "Jane/John",
      "data_recipient": "who the data is sent to",
      "transmission_principle": "how the data is transmitted",
      "source": "source of the seed",
      "source_details": {"...": "backtrace information for seeds collected from regulations or literature"}
   },
   "vignette": {
      "story": "expressive vignette",
      "data_type_concrete": "concrete data type in the vignette",
      "data_subject_concrete": "concrete data subject in the vignette",
      "data_sender_concrete": "concrete data sender in the vignette",
      "data_recipient_concrete": "concrete data recipient in the vignette"
   },
   "trajectory": {
      "user_name": "Jane Doe/John Doe",
      "user_email": "jane.doe@gmail.com/john.doe@gmail.com",
      "user_instruction": "user instruction",
      "toolkits": ["tool 1", "tool 2", "..."],
      "executable_trajectory": "a sequence of agent actions and environment observations",
      "final_action": "the final action type",
      "sensitive_info_items": ["sensitive info item 1", "sensitive info item 2", "..."]
   }
}

You can also access the dataset through Hugging Face Datasets.

The additional data extended from ConfAIde (see Appendix D) and data extended from CultureBank (see Section 5.3) can be found in data/extensibility/.

To view the data points in a more readable way, you can use our data inspection tool. Run the following command in the root directory and upload the JSON file.

streamlit run helper/inspect_data.py

Probing in Question-Answering Format

Run the following command under evaluation/ to evaluate LMs with probing questions.

python probing.py --input-path '<dataset path>' --output-path '<output csv file path>' --model '<model name>' --level '<a list of levels to test>' --start-index 0 --num 1

• --level: The probing level to evaluate. Can include seed, vignette, trajectory, trajectory_enhancing. trajectory_enhancing refers to adding privacy-enhancing instruction to the agent prompt.
• --model: The name of the model to evaluate. We have evaluated gpt-3.5-turbo-1106, gpt-4-1106-preview claude-3-haiku-20240307, claude-3-sonnet-20240229, mistralai/Mistral-7B-Instruct-v0.2, mistralai/Mixtral-8x7B-Instruct-v0.1, HuggingFaceH4/zephyr-7b-beta, meta-llama/Meta-Llama-3-8B-Instruct, meta-llama/Meta-Llama-3-70B-Instruct in our paper.

Evaluating LMs in Action with Agent Setup

Run the following command under evaluation/ to get the final action of a certain LM agent with the given executable trajectory.

python get_final_action.py --input-path '<dataset path>' --output-path '<output csv file path>' --model '<model name>' --prompt-type '<naive or privacy_enhanced>' --start-index 0 --num 1

• --prompt-type: The type of prompt to use. Can be naive or privacy_enhanced.
• --model: See above.

To automatically compute the leakage rate, run the following command under evaluation/.

python evaluate_final_action.py --data-path '<dataset path>' --action-path '<action csv file path>' --step 'judge_leakage' --output-path '<output json file path>' --hf-cache-dir '<cache dir to store the evaluator checkpoint>'

• For reproducibility of our paper, this script uses Mistral-7B-Instruct-v0.2 to judge the data leakage, so it requires GPU resource.

To get the helpfulness rating of the final action, run the following command under evaluation/.

python evaluate_final_action.py --data-path '<dataset path>' --action-path '<action csv file path>' --step 'helpfulness' --output-path '<output json file path>' --hf-cache-dir '<cache dir to store the evaluator checkpoint>'

• For reproducibility of our paper, this script uses Mistral-7B-Instruct-v0.2 to rate the helpfulness, so it requires GPU resource.

Using the Data Construction Pipeline Yourself

If you have your own seeds and want to test how LMs behave in action in the privacy-sensitive scenarios you care, you can use our data construction pipeline. Please check the quality of the constructed data points before using them for evaluation.

Expand Seeds to Vignettes

We expect the seeds to be in the following format (the same with the final dataset):

[
   {
      "name": "unique id",
      "seed": {
         "data_type": "data type",
         "data_subject": "who the data is about",
         "data_sender": "who sends the data",
         "data_sender_name": "Jane/John",
         "data_recipient": "who the data is sent to",
         "transmission_principle": "how the data is transmitted",
         "source": "source of the seed"
      }
   },
   "...more seeds..."
]

Run the following command under data_construction/ to expand the seeds to vignettes.

python seed_to_vignette.py --input-path '<seed json file path>' --output-path '<output path>'

After this step, you will get data points with both seed and vignette fields.

Construct Trajectories

Run the following commands under data_construction/.

1. Prepare the formatted samples for trajectory simulation using the sandbox environment. The input file should contain both the seed and vignette fields.

   python format_vignette_for_trajectory_simulation.py --input-path '<input file path>' --output-path '<output json file path>'

2. Simulate the trajectory using the sandbox environment.

   python simulate_trajectory.py --input-path '<formatted sample file path>' --agent-model-name 'gpt-4-1106-preview or azure-gpt-4-1106' --simulator-model-name 'gpt-4-1106-preview or azure-gpt-4-1106' --critiquer-model-name 'gpt-4-1106-preview or azure-gpt-4-1106' --start-index <start index> --trunc-num <number of samples to simulate>

3. Format the simulated trajectory into the final data point.

   python format_trajectory.py --trajectory-path '<trajectory jsonl file path>' --vignette-path '<vignette json file path>' --output-path '<output json file path>' --use-surgery-kit

4. Extract the sensitive information items from the trajectory so that you can access whether LM agents' final actions leak sensitive information. Please check the quality of the extracted sensitive information items before using them to compute the leakage rate.

   python evaluate_final_action.py --data-path '<trajectory data path>' --step 'extract_secret' --output-path '<the same trajectory data path>' --hf-cache-dir '<cache dir to store the evaluator checkpoint>'

Citation

Please cite our paper if you find our code or dataset useful.

@misc{shao2024privacylensevaluatingprivacynorm,
      title={PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action}, 
      author={Yijia Shao and Tianshi Li and Weiyan Shi and Yanchen Liu and Diyi Yang},
      year={2024},
      eprint={2409.00138},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2409.00138}, 
}