Introducing Privana: A Human-Readable Privacy Policy Tool Powered by AI
In today’s digital landscape, understanding what data your favorite apps collect about you remains a challenge. Privacy policies, often lengthy and complex, are designed to be difficult to interpret, leaving users in the dark regarding how their information is used. To address this issue, I have developed Privana, an innovative tool aimed at demystifying app privacy policies through AI-driven summaries that are accessible and easy to understand.
What Is Privana?
Privana is a web-based application designed to help users gain clear insights into the data collection practices of various apps. By inputting an app name or URL, users can receive a concise, human-readable summary of its privacy policy, highlighting key points such as the types of data collected, usage purposes, and sharing practices.
The Motivation Behind Privana
Privacy policies are essential but often overwhelming documents. Many users are willing to accept data collection in exchange for free services, yet they often lack comprehensive understanding of what they’re agreeing to. My goal with Privana is to bridge this knowledge gap by leveraging large language models (LLMs) to process and present privacy information in a straightforward manner.
How It Works
The core process involves several steps:
- Data Collection: For now, I manually input URLs of the top privacy policies from the most popular apps, stored in a dedicated database.
- Content Retrieval: Using the Perplexity Sonar API, I fetch the full privacy policy text associated with each URL.
- Summarization: The retrieved content is processed through LLMs to generate a summarized version that captures the essential details.
- Manual Refinement: The summaries are then manually cleaned to ensure clarity and accuracy before presenting them to users.
Looking ahead, I plan to automate much of this process. This will involve developing web scraping tools to automatically discover and extract privacy policies, as well as implementing caching mechanisms for quick and efficient responses.
Current Status and Future Plans
While Privana is still a work in progress, the main functionality is operational and user-friendly. The next steps involve scaling the app to handle larger datasets seamlessly through automation, enhancing the UI/UX, and optimizing the backend processes—including automating the summarization pipeline.
Seeking Community Feedback
As I continue refining Privana, I’m eager to hear feedback from developers and privacy-conscious users alike. Suggestions on improving the user interface, experience, or automation methods
