AISHE System Data Protection and Privacy Policy

1. Introduction

This Data Protection and Privacy Policy ("Policy") outlines how the AISHE (Artificial Intelligence System Highly Experienced) operates with respect to data protection principles. This Policy applies to all users of the AISHE system worldwide and demonstrates our commitment to privacy protection in compliance with the European Union's General Data Protection Regulation (GDPR/DSGVO) and other international data protection standards.

2. Data Processing Principles

2.1 Local Processing Architecture

The AISHE system follows a decentralized data processing model:

All AI processing occurs exclusively on the user's local device
No personal or financial data is transmitted to external servers
The system connects directly to the user's selected broker via MetaTrader 4 or other trading platforms
All trading decisions are executed within the user's controlled environment

2.2 Pseudonymization Approach

AISHE implements advanced pseudonymization techniques:

A unique identifier (AISHE ID) is generated based solely on hardware characteristics of the user's device
This ID is created using cryptographic one-way functions from the hardware serial number
The process is non-reversible: it is technically impossible to derive the original hardware serial number from the AISHE ID
No personal information is embedded in or associated with the AISHE ID

3. Data Collection and Processing

3.1 Minimal Data Requirements

AISHE adheres strictly to the data minimization principle:

The system does not collect, process, or store any personal data
No financial information, trading history, or market data leaves the user's device
The only transmitted data is the AISHE ID, which serves solely as a non-identifiable reference

3.2 Purpose Limitation

The AISHE ID serves only these specific purposes:

Payment processing for system usage
Technical support identification (when contacting customer service)
Ticket management for support requests
Connection to the distributed learning network (with user consent)

4. Federated Learning Implementation

4.1 Privacy-Preserving Machine Learning

AISHE implements federated learning with enhanced privacy safeguards:

All model training occurs locally on the user's device
Only encrypted model updates (2048-bit encryption) are shared
No raw data, trading patterns, or financial information is transmitted
The learning process follows strict differential privacy principles

4.2 User Control Over Learning

Users maintain complete control over the learning process:

Explicit opt-in required for participation in collective learning
Users can select which instruments to include in the learning process
All learning parameters are configurable by the user
Complete transparency about learning activities through the AISHE interface

5. Data Security Measures

5.1 Technical Safeguards

AISHE implements robust security measures:

End-to-end encryption for all communication (2048-bit)
Secure local storage of all trading data on the user's device
Regular security audits of the system architecture
Protection against unauthorized access through hardware-based security

5.2 System Integrity

All system components undergo regular integrity verification
Tamper-proof mechanisms prevent unauthorized modification
Secure boot process ensures system authenticity
Regular security updates delivered through verified channels

6. User Rights and Transparency

6.1 User Control

AISHE provides comprehensive user control:

Right to access all system data stored locally
Right to delete all system data with a single command
Clear visibility into system operations through the dashboard
Complete control over data sharing preferences

6.2 Transparency Requirements

Clear documentation of all data processing activities
Plain language explanations of technical processes
Visual representation of data flows within the system
Regular transparency reports available to all users

7. International Compliance Framework

7.1 Global Data Protection Standards

AISHE complies with multiple international data protection frameworks:

GDPR (EU General Data Protection Regulation)
CCPA (California Consumer Privacy Act)
PIPEDA (Canadian Personal Information Protection and Electronic Documents Act)
Other relevant regional data protection laws

7.2 Cross-Border Data Processing

All data remains on the user's local device
No cross-border data transfers of personal information
For system updates and support, only the non-identifiable AISHE ID is used
All distributed learning follows strict anonymization protocols

8. Data Processing Agreement

8.1 Role Clarification

AISHE provider acts as a technology provider, not a data processor
The user remains the sole data controller for all trading activities
No personal data is processed by the AISHE provider
All responsibility for trading data rests with the user and their financial institution

8.2 Third-Party Relationships

Brokers and financial institutions are directly selected and managed by the user
AISHE has no access to or relationship with the user's financial accounts
All connections to trading platforms occur through the user's secure environment

9. Implementation and Compliance

9.1 Compliance Verification

Regular independent audits of the privacy framework
Certification against ISO/IEC 27001 and other relevant standards
Ongoing monitoring of regulatory developments worldwide
Transparent reporting of compliance status

9.2 User Education

Comprehensive documentation explaining privacy features
In-system guidance for optimal privacy configuration
Regular updates on data protection best practices
Clear explanations of technical processes in non-technical language

10. Contact Information

For any questions regarding this Policy or the AISHE system's data protection practices:

AISHE Data Protection Office
Email: info @ aishe24.com
Date: 02.08.2025

This Policy is reviewed and updated annually to ensure continued compliance with evolving data protection standards worldwide. The most current version is always available at https://www.aishe24.com/privacy-policy .