Protocols

The foundation of Aromance lies in its technical protocols. These are the systems and mechanisms that ensure our platform is reliable, transparent, and trustworthy.

1. Authentication Protocol

• We use a Decentralized Identity (DID) system to verify users.

• Each user owns a unique identity key that controls access to their account and data.

• Unlike centralized logins (email/password), DID prevents unauthorized access and eliminates dependency on a single server.

• Authentication is handled through secure blockchain-based identity management, ensuring privacy and security.

2. Data Storage & Encryption

• User profiles, fragrance preferences, and reviews are stored in a distributed network, reducing risks of data leaks.

• All sensitive data is encrypted at rest and in transit.

• Access to personal data is strictly permission-based and requires the user’s private key.

• No central database exists that can be exploited or sold.

3. Transaction & Payment Protocol

• Transactions between buyers and sellers are secured through blockchain smart contracts.

• Payments are executed transparently, with receipts recorded immutably.

• Fraud and chargebacks are minimized since smart contracts automatically enforce rules.

• Future integration with local payment gateways (GoPay, OVO, DANA) will ensure accessibility while maintaining blockchain-level security.

4. Review & Verification Protocol

• Aromance introduces a Proof-of-Stake verification system for both sellers and reviewers.

• To become verified, participants deposit a stake that acts as a guarantee of honesty.

• If misleading or fake information is provided, the stake is lost, ensuring accountability.

• This creates a trust-based ecosystem where every review and seller profile is reliable.

5. AI Recommendation Protocol

• Our AI fragrance consultant analyzes user preferences, personality traits, and purchase history.

• Unlike generic recommendation engines, Aromance AI is trained specifically for perfumes and fragrance notes.

• AI operates within privacy boundaries – it can generate recommendations without exposing user data externally.

• This ensures personalization without compromising security.