Building Privacy into KIN, and Personal AI

Part 4 of 4 in the series "The Road to Privacy in Personal AI".

• Part 1: Privacy in Personal AI

• Part 2: Three Core Challenges in Data Encryption

• Part 3: Solving the holy grail of “Encryption in Use”

• Part 4: Building privacy into KIN, and personal AI

This article explores how we implement data computation and storage while ensuring robust data privacy and user-controlled personal data in our AI system.

Previously, we outlined five different approaches, with technology choices driven by specific use cases and requirements for privacy protection.

Our use case focuses on building a trustworthy personal AI that remembers your conversations, learns from them, maintains data security, and leverages artificial intelligence to become your companion, assistant, coach, or friend.

3 Core Requirements Guiding Our Implementation

Let's examine the three main requirements affecting our privacy-focused development:

1. KIN must be powerful, fast and cost-effective

2. KIN must work around the clock, even with the app closed

3. KIN must be able to use state-of-the-art Machine Learning Models

1. Power, Speed, and Cost-Effectiveness for KIN

This means prioritizing local, on-device computation. Cloud computing can be costly, and network latency can impact real-time performance.

Your smartphone provides remarkable computational power. Leveraging this for local processing delivers superior functionality and user experience while enhancing privacy protection and reducing cloud costs.

We've observed significant advancements in "Edge Machine Learning" with Apple's Neural Engine and similar technologies from other providers, plus browser APIs enabling direct GPU access.

2. KIN Must Work Around the Clock

While local processing excels, some AI applications require background operations without an active app. This includes delegating tasks to your AI system for later reporting or handling periodic data collection.

Therefore, KIN must support background and asynchronous processing of user data.

3. KIN requires state-of-the-art ML models

While Edge ML continues advancing, running sophisticated Large Language Models (LLMs) for top-tier AI systems remains impractical on edge devices, particularly mobile ones.

KIN therefore requires a secure, privacy-protective approach to running models beyond device capabilities.

Deciding on Data Computation

Based on our requirements and privacy concerns, we've identified two primary approaches:

Local/On-Device computation

Confidential Cloud computation

We've implemented a hybrid architecture combining local-first processing with confidential cloud computing.

We prioritize local resources for data and computing, only utilizing confidential cloud (Trusted Execution Environments, or TEEs) when necessary, such as for LLM inference or long-running tasks.

We're also monitoring emerging technologies in Fully Homomorphic Encryption (FHE), which we plan to integrate once practical. FHE integration with LLMs is approximately five years away, aligning with our development timeline.

Data Storage and Privacy

Our data computation strategy directly influences storage requirements and accessibility. Given our core requirements, we implement:

1. For Edge ML →  Your data must be available locally, so stored on your device

2. For Cloud ML → Your data should be stored in the cloud

Because KIN operates in the background, data must be cloud-accessible while maintaining strong privacy protection.

Cloud storage presents two key challenges:

1. Privacy-protective synchronization between local and cloud storage

2. Secure data access for cloud-based AI systems without compromising sensitive information

5 Essential Components for Privacy-Focused Data Storage

In developing KIN, a hybrid data storage strategy has been adopted, balancing local-first storage with cloud capabilities to ensure privacy, efficiency, and functionality. Here's a concise overview of the key components:

1. Local-first Data

KIN prioritizes on-device storage, enhancing privacy protection and reducing latency for faster processing. This approach reinforces data sovereignty and performance.

2. Synchronization

Through master-to-master synchronization with server coordination, KIN ensures data consistency across devices while preventing unauthorized access.

3. End-to-End Encryption (E2E)

All data transmitted and stored in the cloud employs end-to-end encryption, ensuring sensitive data remains accessible only to authorized users.

4. Advanced Data Structures

KIN implements sophisticated data structures, including vector embeddings, optimizing AI processes while maintaining privacy protection.

5. Permission Layer

A comprehensive permission system employing multiple keys ensures users control access to their personal data, maintaining data privacy throughout the system.

This streamlined approach ensures that KIN is not only a powerful AI companion but also a guardian of user privacy and data security, leveraging the best of local and cloud technologies.

Your Data is Your Business, Not Ours

User-controlled data remains paramount. KIN empowers users by ensuring you maintain complete control over your personal data. We prevent data lock-in, enabling you to access and utilize your information as you choose.

We don’t want to lock your data in, you should always be able to access it and use it as you please. 

Remember: Your data is your business, not ours.

KIN is more than a personal AI

KIN is not just a technological advancement, but a step towards a more secure and private digital future and a future with personal AIs. 

If you value your privacy and wish to take control of your data, we invite you to join the KIN and spread the word.

By getting a KIN, using it, and inviting others on board as well, you'll be part of a movement prioritizing security, privacy, and user empowerment in the digital age.

Go to KIN, your personal AI for your private life