Proposal for a Personal Assistant API

Abstract

We propose the development of a standardized Personal Assistant API. This API will enable web browsers to act as personal assistants for users, providing a secure and privacy-respecting interface for collecting, storing, and selectively sharing personal information with websites. This will enhance user personalization, privacy, and control while maintaining compliance with global privacy standards and aligning with W3C Decentralized Identity (DID) and Data Privacy Vocabulary and Controls Community Group (DPVCG) standards. Additionally, this API will support optional client-side large language models (LLMs) to provide enhanced functionality for personalization and data processing directly within the browser.

Introduction

The modern web thrives on personalization, but current methods of collecting user data often compromise privacy and security. Users have little visibility or control over how their data is collected and shared. A browser-integrated personal assistant, governed by a standardized API, can address these issues by securely storing user data locally and enabling controlled data sharing with websites. By incorporating W3C DID and DPVCG standards, this proposal ensures decentralized, verifiable identities and a structured approach to privacy and data protection. Furthermore, integrating optional client-side LLMs will empower websites to process user data locally, reducing dependency on server-side processing and enhancing privacy.

Goals

• Provide users with a centralized, browser-native personal assistant to manage their data.
• Enable decentralized, verifiable identity solutions through W3C DID standards.
• Implement privacy controls and transparency using W3C DPVCG data privacy vocabularies.
• Allow websites to request user information securely and transparently.
• Support optional client-side LLMs for enhanced personalization and local data processing.
• Enhance privacy by storing data locally and minimizing unnecessary sharing.
• Facilitate compliance with global privacy regulations (e.g., GDPR, CCPA).

Use Cases

1. E-Commerce Personalization:
   • A shopping website requests user preferences (e.g., clothing size, favorite colors) to tailor product recommendations.
   • Verifiable credentials (e.g., age range, membership status) are shared via DID-compliant mechanisms, with privacy terms structured using DPVCG.
   • The website optionally utilizes a client-side LLM to analyze preferences and provide context-aware recommendations without transmitting sensitive data to a server.
2. Content Localization:
   • A news website requests the user’s language and location preferences for localized content delivery.
   • The user’s location credential is shared securely through the personal assistant using DID and privacy preferences tagged with DPVCG terms.
   • A client-side LLM, if available, adapts content summaries or headlines in real-time based on user preferences.
3. Autofill and Form Suggestions:
   • The browser assistant autofills forms with user-consented data such as name, address, or contact details, verified through decentralized credentials and documented privacy policies using DPVCG.
   • A client-side LLM, if supported, assists by interpreting ambiguous field labels and mapping them to stored user data.

Design Principles

1. User-Centric Privacy:

   • Users have full control over what data is shared and with whom.
   • Default settings prioritize privacy.

2. Decentralized Identity:

   • Integrate W3C DID standards to ensure that users’ identities are verifiable, self-sovereign, and interoperable.

3. Privacy Vocabulary Compliance:

   • Utilize W3C DPVCG standards to define and communicate data processing purposes, legal bases, and other privacy-related metadata.

4. Transparency:

   • Clear logs of data requests and sharing activities.
   • Websites must declare why data is being requested and how it aligns with DPVCG terms.

5. Security:

   • Data stored locally with strong encryption.
   • API mechanisms to prevent unauthorized access.

6. Client-Side Intelligence:

   • Support optional client-side LLMs for local data processing, ensuring data privacy while enhancing functionality.

7. Interoperability:

   • Ensure compatibility across major browsers and platforms.
   • Align with W3C DID and DPVCG standards for decentralized identity and privacy compliance.

API Overview

The Browser-Based Personal Assistant API would provide:

User Data Storage

• Secure storage for personal data within the browser.
• Categories of data: Preferences, Demographics, Location, Contact Information, Verifiable Credentials, etc.
• Privacy-related metadata structured using DPVCG vocabularies.

Permission Management

• Granular controls for users to approve or deny specific data requests.
• Option for one-time or persistent permissions.
• Verifiable credential sharing compliant with W3C DID standards, with privacy metadata tagged using DPVCG.

Data Request Interface

Websites can request data using the API with the following workflow:

1. Request Submission:

   navigator.personalAssistant.requestData({
       fields: ['language', 'location'],
       purpose: 'Localize content',
       didOptions: {
           verify: true,
           types: ['LocationCredential'],
       },
       privacyMetadata: {
           purpose: 'localization',
           legalBasis: 'userConsent',
           retentionPeriod: 'session',
       },
       useLLM: true, // Optional: Indicates the use of client-side LLMs if supported by the user agent
   });

2. User Prompt: The browser prompts the user to approve or deny the request, ensuring any verifiable credentials are verified via DID methods and privacy metadata is clearly communicated.

3. Response Handling: If approved, the browser shares the requested data:

   {
       language: 'en-US',
       location: {
           credential: 'https://example.com/verifiable-credential/location',
           verified: true,
       },
       privacyMetadata: {
           purpose: 'localization',
           legalBasis: 'userConsent',
           retentionPeriod: 'session',
       },
       llmOutput: {
           personalizedSummary: 'Localized news content for your area.',
       },
   }

Optional Client-Side LLM Support

• Initialization: The API enables websites to invoke pre-installed client-side LLMs within the browser if supported by the user agent.
• Use Cases:
  • Natural language processing for summarizing content.
  • Analyzing user preferences and generating context-aware suggestions.
  • Assisting in interpreting ambiguous user inputs.
• Privacy:
  • All processing is done locally, ensuring sensitive data is never transmitted to external servers.
• Optional Implementation:
  • User agents are not required to implement LLM functionality but should allow its use if available.

Security and Privacy

1. Data Encryption:

   • All data stored locally is encrypted.

2. Anonymization:

   • The assistant can anonymize data (e.g., sharing age range instead of exact age).

3. Decentralized Identity:

   • Verifiable credentials are issued and verified via W3C DID standards.

4. Privacy Metadata:

   • All data requests and processing are described using DPVCG vocabularies, ensuring transparency and compliance.

5. Client-Side Processing:

   • Optional LLMs process data locally, minimizing risks associated with data transmission.

6. Compliance:

   • Built-in mechanisms for GDPR/CCPA compliance, including data access logs and deletion requests.

7. Rate Limiting:

   • Limit the frequency and scope of data requests to prevent abuse.

Implementation Considerations

1. Browser Integration:

   • Native support in major browsers (e.g., Chrome, Firefox, Safari).
   • LLM functionality should be optional and dependent on the user agent’s capabilities.

2. Developer Adoption:

   • Provide clear documentation and tools for web developers.

3. Standardization:

   • Collaborate with W3C to define the API’s scope and standards.
   • Align with W3C DID, DPVCG, and emerging client-side LLM standards to ensure interoperability.

Conclusion

The Browser-Based Personal Assistant API represents a significant step forward in balancing personalization with privacy. By empowering users to control their data while enabling websites to access it securely and transparently, this API will foster a more trustworthy and user-centric web. Incorporating W3C DID, DPVCG standards, and optional client-side LLMs ensures that decentralized, verifiable identities, structured privacy vocabularies, and advanced local processing capabilities are at the heart of this solution.

Next Steps

1. Form a W3C Community Group to refine the proposal.
2. Conduct feasibility studies and prototype development.
3. Engage with browser vendors and web developers for feedback.

Acknowledgments

We appreciate the efforts of the W3C and its members in fostering an open and interoperable web. This proposal builds upon existing standards and aims to complement ongoing privacy, decentralized identity, and client-side AI initiatives.