💕 Privacy-Preserving Dating with PLGL

The Privacy Problem in Dating Apps

How Privacy-Preserving Dating Works

1. Train Your Preferences (No Real Photos)

Users rate AI-generated faces to teach the system their preferences:

def train_user_preferences(self, user_id: str, n_training_faces=50):
    """
    User trains preferences on AI-generated faces
    No real user photos are ever shown during training!
    """
    training_data = []
    
    print("Please rate these AI-generated faces...")
    
    for i in range(n_training_faces):
        # Generate random face
        z = torch.randn(1, self.latent_dim)
        ai_face = self.face_generator(z)
        
        # User rates (swipe right/left)
        rating = get_user_rating(ai_face)  # 1 for like, 0 for dislike
        
        training_data.append({
            'latent': z,
            'rating': rating
        })
    
    # Train preference model
    self.train_preference_model(user_id, training_data)
    print(f"Learned your preferences from {n_training_faces} ratings!")

2. Secure Photo Encoding

Your photos are immediately encoded and the originals are discarded:

def onboard_new_user(self, user_id: str, user_photos: List[Image]):
    """
    Encode user photos to latent space
    Original photos are NEVER stored!
    """
    latent_representations = []
    
    for photo in user_photos:
        # Encode to latent space
        with torch.no_grad():
            latent = self.encoder(photo)
            latent_representations.append(latent)
        
        # Photo is immediately discarded after encoding
        del photo
    
    # Average latent representations
    user_latent = torch.stack(latent_representations).mean(dim=0)
    
    # Add privacy noise
    privacy_noise = torch.randn_like(user_latent) * 0.1
    secure_latent = user_latent + privacy_noise
    
    # Store only the latent representation
    profile = DatingProfile(
        user_id=user_id,
        latent_representation=secure_latent,
        preference_model=preference_model
    )
    
    print(f"Photos encoded and discarded. Privacy preserved!")

3. Latent Space Matching

Find compatible matches without comparing actual photos:

def find_matches(self, user_id: str, top_k: int = 10):
    """
    Find compatible matches in latent space
    No photos are compared - only mathematical representations!
    """
    user_profile = self.users[user_id]
    user_pref_model = user_profile.preference_model
    
    potential_matches = []
    
    for other_id, other_profile in self.users.items():
        if other_id == user_id:
            continue
        
        # Bidirectional compatibility check
        # Does user like other's latent representation?
        user_likes_other = user_pref_model(
            other_profile.anonymize_latent()
        ).item()
        
        # Would other like user's latent representation?
        other_likes_user = other_profile.preference_model(
            user_profile.anonymize_latent()
        ).item()
        
        # Mutual compatibility score
        compatibility = (user_likes_other + other_likes_user) / 2
        
        potential_matches.append({
            'user_id': other_id,
            'compatibility': compatibility,
            'mutual_interest': min(user_likes_other, other_likes_user)
        })
    
    # Sort by compatibility
    matches = sorted(potential_matches, 
                    key=lambda x: x['compatibility'], 
                    reverse=True)[:top_k]
    
    return matches

4. Secure Photo Exchange

Photos are only reconstructed after mutual consent:

def request_photo_reveal(self, user1_id: str, user2_id: str):
    """
    Both users must consent before photos are revealed
    """
    # Check mutual match
    if not self.is_mutual_match(user1_id, user2_id):
        return "Match required before photo exchange"
    
    # Both must explicitly consent
    consent_key = f"{user1_id}:{user2_id}"
    
    if self.has_both_consented(consent_key):
        # Generate photos from latent representations
        user1_face = self.generate_from_latent(
            self.users[user1_id].latent_representation
        )
        user2_face = self.generate_from_latent(
            self.users[user2_id].latent_representation
        )
        
        # Secure exchange
        return self.secure_photo_exchange(user1_face, user2_face)
    
    return "Waiting for mutual consent"

5. Additional Privacy Features

class EnhancedPrivacy:
    def differential_privacy_training(self, user_data):
        """Add noise to training for differential privacy"""
        # Add calibrated noise to gradients
        dp_noise = torch.randn_like(gradients) * self.privacy_budget
        private_gradients = gradients + dp_noise
        
    def homomorphic_matching(self, encrypted_latent1, encrypted_latent2):
        """Compute compatibility on encrypted data"""
        # Match without decrypting latent representations
        encrypted_score = self.he_scheme.compute(
            encrypted_latent1, 
            encrypted_latent2
        )
        return encrypted_score
    
    def zero_knowledge_verification(self, user_id):
        """Verify user properties without revealing them"""
        # Prove age/location without exposing actual values
        proof = self.zk_prove(
            statement="user is 18+ and in same city",
            witness=user_private_data
        )
        return proof

Why This Is Revolutionary

The Original Vision

"Perfection is just a few swipes away" - SkinDeep.ai (2018)

This application extends the original SkinDeep.ai concept. Instead of just finding your perfect AI-generated face, it finds your perfect real match while preserving complete privacy. The same preference learning that could generate your ideal face can now find real people who match that ideal.