Homomorphic encryption

How Homomorphic encryption Works

  User Click/Impression       Encrypted Traffic Data      Secure Analysis Engine       Encrypted Result            Action
  +------------------+         +--------------------+     +--------------------+      +----------------+       +----------------+
  | IP: 8.8.8.8      | ------> | Ciphertext: XyZ... | --> | Perform Operations | -- > | Result: e(Fraud) | ----> | Block/Allow    |
  | User-Agent: XYZ  |         | Ciphertext: AbC... |     | (e.g., Aggregation,|      | Result: e(Valid) |       | Traffic        |
  | Timestamp: 12345 | ------> | Ciphertext: 1jK... | --> |   Scoring, ML)     | -- > +----------------+       +----------------+
  +------------------+         +--------------------+     +--------------------+
                                      (Encryption)            (Computation on         (Decrypted only by
                                                                Encrypted Data)         authorized party)

Homomorphic encryption provides a revolutionary way to analyze sensitive advertising traffic without compromising the privacy of the underlying data. The process allows a traffic security system to perform complex calculations, such as fraud scoring or anomaly detection, directly on encrypted information, ensuring that the raw, plaintext data is never exposed to the analysis environment. This is crucial for adhering to privacy regulations and protecting proprietary business data.

Data Encryption at the Source

When a user interacts with an ad, key data points such as their IP address, user agent, device ID, and interaction timestamps are collected. Before this data is sent for analysis, it is encrypted using a public key. This transforms the sensitive plaintext information into a ciphertext—an unreadable format. The critical aspect is that this encryption scheme is homomorphic, meaning it preserves the mathematical structure of the original data, allowing specific computations to be performed on it.

Secure Computation in the Cloud

The encrypted traffic data is then sent to a processing environment, typically a cloud server, where the fraud detection logic resides. This environment does not have the private key needed to decrypt the data. Instead, it runs its analyses—such as aggregating clicks, checking frequencies, or executing a machine learning model—directly on the ciphertext. Because the computations are homomorphic, the operations on the encrypted data mirror the operations that would have been performed on the plaintext data.

Fraud Analysis and Secure Verdict

The analysis engine processes the encrypted data to identify patterns indicative of fraud, such as abnormally high click rates from a single encrypted source or geographic mismatches between encrypted IP location data and stated user location. The result of this computation is itself an encrypted value—for example, an encrypted “fraud score” or a simple “valid” or “invalid” flag. This encrypted result is then sent back to the data owner or an authorized system component.

Diagram Element Breakdown

User Click/Impression

This block represents the initial event in the ad pipeline. It contains raw, sensitive data points (IP, user agent, etc.) that need to be analyzed for fraud but must also be protected. This is the plaintext input into the system.

Encrypted Traffic Data

This shows the state of the data after it has been encrypted with a homomorphic public key. Each piece of information is now an unreadable ciphertext. This step is essential for protecting data privacy before it leaves a secure environment for analysis.

Secure Analysis Engine

This is the core component where the fraud detection logic operates. It performs mathematical operations (e.g., addition, multiplication, comparisons) directly on the encrypted ciphertexts. Its ability to work on data it cannot read is the central function of homomorphic encryption.

Encrypted Result

The output of the analysis engine is also encrypted. This ensures that the outcome of the fraud check remains confidential until it is received by a party holding the corresponding private key. This prevents any intermediate systems from learning the fraud verdict.

Action

This final block represents the business logic that is executed after the encrypted result is decrypted by an authorized party (e.g., the advertiser’s internal system). Based on the decrypted verdict, the system can take action, such as blocking a fraudulent IP address or validating a legitimate conversion.

🧠 Core Detection Logic

Example 1: Encrypted IP Frequency Analysis

This logic checks for an abnormally high number of clicks from a single source within a short timeframe, a common sign of bot activity. By operating on encrypted IP addresses, the system can count occurrences of the same IP without ever knowing the actual IP address, thus preserving user privacy.

// Assume IP addresses are homomorphically encrypted
FUNCTION analyze_encrypted_frequency(encrypted_traffic_data, time_window):
  // Group clicks by encrypted IP address
  ip_groups = group_by(encrypted_traffic_data, 'encrypted_ip')

  FOR each group IN ip_groups:
    // Homomorphically count clicks for each encrypted IP
    encrypted_click_count = homomorphic_sum(group.clicks)

    // Decrypt the result with the private key
    decrypted_count = decrypt(encrypted_click_count)

    IF decrypted_count > CLICK_THRESHOLD:
      mark_as_fraud(group.encrypted_ip)
  RETURN

Example 2: Secure Geolocation Mismatch Detection

This logic compares the geolocation derived from a user’s IP address with self-reported location data (e.g., in a user profile) to detect inconsistencies. The entire comparison is done on encrypted location data, allowing fraud detection without exposing sensitive user locations.

// Assume location data (IP-based and user-reported) is encrypted
FUNCTION check_geo_mismatch(encrypted_ip_location, encrypted_user_location):
  // Homomorphically perform an equality check on the encrypted data
  // The result is an encryption of 1 if they are equal, 0 otherwise
  encrypted_match_result = homomorphic_compare_equal(encrypted_ip_location, encrypted_user_location)

  // Decrypt the result to get the boolean outcome
  is_match = decrypt(encrypted_match_result)

  IF is_match == FALSE:
    return "High Fraud Risk"
  ELSE:
    return "Low Fraud Risk"

Example 3: Private Set Intersection for Botnet Detection

This technique allows a fraud detection service to check if incoming traffic IPs are on a known botnet blacklist without either party revealing their lists. The service and the advertiser can find matching fraudulent IPs without exposing the advertiser’s entire visitor list or the service’s full blacklist.

// PSI allows finding the intersection of two sets without revealing the elements
FUNCTION find_botnet_ips(advertiser_encrypted_ips, service_encrypted_botnet_ips):
  // The PSI protocol computes the intersection of the two encrypted sets
  encrypted_intersection = private_set_intersection(
    advertiser_encrypted_ips,
    service_encrypted_botnet_ips
  )

  // The advertiser can decrypt the result to get the list of their IPs that are on the blacklist
  fraudulent_ips_on_my_site = decrypt(encrypted_intersection)

  FOR each ip IN fraudulent_ips_on_my_site:
    block_traffic_from(ip)
  RETURN

📈 Practical Use Cases for Businesses

• Secure Data Collaboration: Advertisers, publishers, and security vendors can pool their encrypted traffic data to build more accurate fraud detection models without exposing sensitive customer information or proprietary data to each other.
• Privacy-Compliant Campaign Analytics: Businesses can analyze user behavior across campaigns and platforms on encrypted data, enabling attribution and optimization while adhering to strict privacy laws like GDPR and CCPA.
• Protected AI Model Training: Fraud detection models can be trained on diverse, encrypted datasets from multiple sources. This improves the model’s accuracy against new threats without centralizing or exposing the raw training data.
• Confidential Ad Targeting: Retailers and brands can analyze encrypted customer data to create targeted segments, ensuring that personalized ads are delivered without compromising individual user privacy.

Example 1: Secure Cross-Campaign Analysis

A business runs multiple ad campaigns and wants to identify bots that click on ads across all of them. Using homomorphic encryption, it can sum up the clicks associated with a single encrypted user ID across different campaigns to find patterns of non-human behavior without linking the activity to a real person.

// User IDs and campaign data are encrypted
FUNCTION analyze_cross_campaign_behavior(encrypted_user_sessions):
  // Group sessions by encrypted user ID
  user_groups = group_by(encrypted_user_sessions, 'encrypted_user_id')

  FOR each user_group IN user_groups:
    // Homomorphically sum clicks across different campaigns for one user
    total_clicks_encrypted = homomorphic_sum([session.clicks for session in user_group])
    unique_campaigns_encrypted = homomorphic_count_distinct([session.campaign_id for session in user_group])

    // Decrypt results for analysis
    total_clicks = decrypt(total_clicks_encrypted)
    unique_campaigns = decrypt(unique_campaigns_encrypted)

    IF total_clicks > 50 AND unique_campaigns > 10:
      flag_user_as_suspicious(user_group.encrypted_user_id)

Example 2: Encrypted Conversion Time Analysis

This logic identifies fraudulent conversions by calculating the time between an ad click and a conversion event (e.g., a purchase or sign-up) on encrypted timestamps. An impossibly short duration (e.g., less than a second) indicates automated bot activity.

// Timestamps are encrypted but subtraction is possible
FUNCTION analyze_conversion_time(encrypted_click_timestamp, encrypted_conversion_timestamp):
  // Homomorphically calculate the difference between the two timestamps
  encrypted_duration = homomorphic_subtract(encrypted_conversion_timestamp, encrypted_click_timestamp)

  // Decrypt the resulting duration
  duration_seconds = decrypt(encrypted_duration)

  IF duration_seconds < MINIMUM_VALID_DURATION:
    return "Fraudulent Conversion"
  ELSE:
    return "Valid Conversion"

🐍 Python Code Examples

Simulating Encrypted Click Aggregation

This code simulates how a server could sum click counts from different sources without decrypting them. A simple `EncryptedValue` class mimics the behavior of homomorphic encryption, allowing addition on the encrypted objects to get an encrypted sum, which is only decrypted at the end.

class EncryptedValue:
    """A simple simulation of a homomorphically encrypted integer."""
    def __init__(self, plaintext_value, public_key):
        # In a real scenario, this would be a complex cryptographic operation
        self._ciphertext = (plaintext_value + public_key) % 1000  # Simplified encryption
        self.public_key = public_key

    def __add__(self, other):
        # Homomorphically add two encrypted values
        new_ciphertext = (self._ciphertext + other._ciphertext)
        new_encrypted_value = EncryptedValue(0, self.public_key)
        new_encrypted_value._ciphertext = new_ciphertext
        return new_encrypted_value

def decrypt(encrypted_value, private_key):
    """Decrypts the value using a private key."""
    # Simplified decryption; assumes public_key + private_key allows revealing the secret
    return (encrypted_value._ciphertext - (2 * encrypted_value.public_key)) % 1000

# --- Usage ---
PUBLIC_KEY = 123
PRIVATE_KEY = 77 # Simplified for demonstration

# Clicks from different ad placements (encrypted at the source)
clicks_source_1 = EncryptedValue(15, PUBLIC_KEY)
clicks_source_2 = EncryptedValue(22, PUBLIC_KEY)

# Server computes the sum on encrypted data
encrypted_total = clicks_source_1 + clicks_source_2

# The owner with the private key decrypts the final result
decrypted_total = decrypt(encrypted_total, PRIVATE_KEY)
print(f"Total clicks calculated from encrypted data: {decrypted_total}")

Logic for Scoring Encrypted Traffic

This example provides a conceptual function for scoring traffic based on several "encrypted" metrics. The function applies weights to these metrics and calculates a fraud score, demonstrating how a decision model could operate on data that remains encrypted throughout the process.

class EncryptedMetric:
    """Simulates an encrypted metric that can be used in weighted calculations."""
    def __init__(self, value):
        # In reality, this would be an FHE-encrypted value
        self.encrypted_value = value # Keeping it simple for the example

    def __mul__(self, weight):
        # Simulate multiplying an encrypted value by a plaintext weight
        self.encrypted_value *= weight
        return self

# --- Usage ---
def calculate_fraud_score(encrypted_metrics):
    """
    Calculates a fraud score based on a list of encrypted metrics and plaintext weights.
    The final score remains "encrypted" until decrypted by the owner.
    """
    weights = {'click_freq': 0.5, 'session_time': -0.2, 'geo_mismatch': 0.7}

    # Simulate homomorphic multiplication and addition
    score_click = encrypted_metrics['click_freq'] * weights['click_freq']
    score_session = encrypted_metrics['session_time'] * weights['session_time']
    score_geo = encrypted_metrics['geo_mismatch'] * weights['geo_mismatch']

    # The final score is an "encrypted" object
    final_score = EncryptedMetric(0)
    final_score.encrypted_value = score_click.encrypted_value + score_session.encrypted_value + score_geo.encrypted_value
    return final_score

# Assume these metrics are encrypted
traffic_metrics = {
    'click_freq': EncryptedMetric(8),      # High click frequency
    'session_time': EncryptedMetric(2),     # Very short session time
    'geo_mismatch': EncryptedMetric(1)      # Geo mismatch detected (1=true)
}

# The server calculates the score without seeing the data
encrypted_score = calculate_fraud_score(traffic_metrics)

# Only the owner can "decrypt" and see the final score
# In this simulation, we just view the internal value
fraud_score = encrypted_score.encrypted_value
print(f"Calculated fraud score on encrypted metrics: {fraud_score:.2f}")

Types of Homomorphic encryption

• Partially Homomorphic Encryption (PHE): This type supports a single mathematical operation (either addition or multiplication) an unlimited number of times on encrypted data. It is less complex and faster, making it suitable for specific fraud detection tasks like securely summing up clicks or transactions.
• Somewhat Homomorphic Encryption (SHE): This type can handle a limited number of both addition and multiplication operations. It is more versatile than PHE but is constrained by the "depth" of the calculations, making it useful for fraud models that are not overly complex.
• Fully Homomorphic Encryption (FHE): FHE is the most powerful type, supporting an unlimited number of any kind of computation on encrypted data. This makes it ideal for running complex machine learning algorithms for fraud detection, though it is the most computationally intensive and slowest of the types.
• Levelled Fully Homomorphic Encryption: This is a practical variant of FHE where the complexity and number of computations are set in advance. By defining these parameters, it becomes more efficient than an open-ended FHE scheme, making it a viable option for structured ad fraud analysis pipelines.

🛡️ Common Detection Techniques

• Private Set Intersection (PSI): This technique allows two parties to compare lists to find common entries without revealing the contents of the lists to each other. It's used to check a list of traffic sources against a known botnet blacklist securely.
• Secure Multi-Party Computation (SMPC): Multiple entities (e.g., advertiser, publisher, ad network) can jointly compute a function over their private inputs without revealing those inputs. This is used to collaboratively analyze traffic and identify fraud patterns across platforms.
• Encrypted Traffic Scoring: This involves applying a fraud detection model to encrypted data points like IP addresses, user agents, and click timestamps. The system calculates a fraud score without ever decrypting the sensitive user data, protecting privacy while assessing risk.
• Blindfolded Behavioral Analysis: This technique performs computations on encrypted behavioral metrics, such as click frequency, session duration, or mouse movement patterns. It allows for the identification of non-human, bot-like behavior while the user's actual actions remain private.

🧰 Popular Tools & Services

Tool	Description	Pros	Cons
Privacy-Preserving Analytics Suite	A service that allows businesses to upload encrypted data and run analytics or ML models to detect fraud, ensuring data remains confidential during processing.	Strong data privacy compliance; enables analysis on sensitive datasets.	High computational overhead; can be slower and more expensive than traditional analytics.
Secure Data Clean Room	A platform where multiple parties can securely combine and analyze their encrypted first-party datasets to find overlapping customers or detect cross-domain fraud.	Facilitates secure data collaboration; unlocks powerful insights without sharing raw data.	Requires agreement and integration between all participating parties; can be complex to set up.
FHE-Powered Threat Intelligence	A service that uses homomorphic encryption to match a company's traffic logs against a global threat database without exposing the company's private data.	Real-time threat detection with maximum privacy; protects proprietary company data.	Performance can be a bottleneck; effectiveness depends on the quality of the threat database.
Confidential ML Platform	A machine learning platform that allows training and inference of fraud detection models directly on encrypted data, protecting both the data and the model's algorithm.	Protects intellectual property (the model) and sensitive data; enables privacy-safe AI.	Extremely resource-intensive; limited to certain types of ML models; requires deep expertise.

📊 KPI & Metrics

When deploying homomorphic encryption for fraud protection, it is vital to track metrics that measure not only the technical performance and accuracy of the detection but also the impact on business outcomes. This ensures the solution is both effective at stopping fraud and efficient in terms of cost and performance.

Metric Name	Description	Business Relevance
Fraud Detection Rate	The percentage of actual fraudulent activities correctly identified by the system.	Measures the core effectiveness of the solution in protecting ad spend and campaign integrity.
False Positive Rate	The percentage of legitimate user interactions incorrectly flagged as fraudulent.	A high rate can lead to blocking real customers and losing potential revenue, impacting user experience.
Computation Overhead / Latency	The additional processing time required to perform fraud analysis on encrypted data versus plaintext.	Directly impacts infrastructure costs and determines the feasibility of using the system for real-time detection.
Ciphertext Size Increase	The factor by which data size increases after being encrypted.	Affects data storage and transmission costs, which can be significant at scale.
Return on Ad Spend (ROAS) Lift	The improvement in ROAS for campaigns protected by the homomorphic encryption solution.	Demonstrates the direct financial value and ROI of implementing the advanced fraud protection system.

These metrics are typically monitored through real-time dashboards that pull data from system logs and analytics platforms. Alerts are often configured to flag anomalies, such as a sudden spike in computation latency or a deviation in the fraud detection rate. This continuous feedback loop is crucial for optimizing the fraud detection models and rules, ensuring the system remains both effective and efficient over time.

🆚 Comparison with Other Detection Methods

Data Privacy and Security

Compared to signature-based detection and standard behavioral analytics, which require access to plaintext data, homomorphic encryption offers superior data privacy. It allows third-party fraud detection services to analyze traffic without ever seeing the sensitive user information, which is a significant advantage for regulatory compliance and protecting customer trust. Other methods expose raw data during analysis, creating a potential privacy risk.

Computational Cost and Speed

Homomorphic encryption is extremely computationally intensive, making it significantly slower than other methods. Signature-based filtering is the fastest, as it involves simple pattern matching. Behavioral analytics has a moderate overhead. The high latency of homomorphic encryption currently makes it more suitable for post-analysis and model training rather than real-time blocking, where speed is critical.

Effectiveness Against New Threats

When combined with machine learning, homomorphic encryption is highly effective against new and evolving fraud tactics because it enables complex analysis on rich datasets. Signature-based methods are inherently reactive and can only detect known threats. Behavioral analytics is also very effective at finding new anomalies, but homomorphic encryption has the unique ability to do so on pooled, encrypted data from multiple sources, potentially identifying large-scale attacks earlier.

Ease of Integration

Integrating homomorphic encryption into an existing ad tech stack is complex and requires specialized cryptographic expertise. Standard signature-based rules or behavioral analytics systems are generally easier to implement and maintain. The complexity of managing encryption keys and ensuring the mathematical stability of homomorphic operations presents a higher barrier to adoption for many organizations.

⚠️ Limitations & Drawbacks

While powerful for privacy, homomorphic encryption has practical drawbacks that can make it inefficient or unsuitable for certain click fraud protection scenarios. Its primary weaknesses relate to performance, complexity, and scale, which can limit its use in real-time, high-throughput environments.

• High Computational Overhead: Performing calculations on encrypted data is thousands of times slower than on plaintext, making real-time fraud detection challenging.
• Significant Data Expansion: Encrypted data is much larger than plaintext, leading to increased storage and bandwidth costs, especially for large-scale traffic analysis.
• System Complexity: Implementing and managing a homomorphic encryption system requires deep cryptographic expertise and careful handling of keys and parameters.
• Noise Growth: In most schemes, each operation adds "noise" to the ciphertext. Too many consecutive operations can render the final result undecipherable if not properly managed.
• Limited Supported Operations: While fully homomorphic schemes exist, they are the slowest. More practical, faster schemes may only support a limited set of mathematical operations, which can constrain the complexity of fraud detection algorithms.

For these reasons, hybrid detection strategies that combine homomorphic encryption for offline, privacy-critical analysis with faster methods like signature-based filtering for real-time blocking are often more practical.

❓ Frequently Asked Questions

Can homomorphic encryption stop all types of click fraud?

No, it cannot stop all types of fraud. Homomorphic encryption is a tool that enables privacy-preserving analysis. Its effectiveness depends on the underlying fraud detection logic (e.g., the algorithms and models) that runs on the encrypted data. It is a powerful enabler for secure analysis, not a fraud detection method in itself.

Is homomorphic encryption used for real-time ad traffic filtering?

Generally, no. Due to its high computational overhead, homomorphic encryption is currently too slow for real-time, large-scale traffic filtering where millisecond latency is required. It is more commonly used for offline analysis, model training, or batch processing where privacy is the primary concern and performance is secondary.

How does homomorphic encryption affect data storage and processing costs?

It significantly increases both. Ciphertexts are much larger than the original plaintext data, leading to higher storage and bandwidth costs. The computational intensity of performing operations on encrypted data also requires more powerful (and expensive) processing infrastructure compared to traditional methods.

Do I need to be a cryptographer to use a service with homomorphic encryption?

Not necessarily. While building a system from scratch requires deep expertise, many companies are developing platforms and tools that abstract away the complexity. For end-users of a fraud detection service that uses homomorphic encryption, the experience is often seamless, as the encryption and computation happen in the background.

How is homomorphic encryption different from other privacy technologies like differential privacy?

Homomorphic encryption allows for exact computations on encrypted data, with the result being precise after decryption. Differential privacy, on the other hand, adds statistical "noise" to datasets to protect individual identities, meaning the results of an analysis are approximate, not exact. They can be used together but solve different privacy problems.

🧾 Summary

Homomorphic encryption is an advanced cryptographic technique that enables computation on encrypted data, fundamentally changing how privacy is managed in ad fraud detection. It allows traffic security systems to analyze sensitive click and user data for fraudulent patterns without ever decrypting it. This ensures compliance with privacy regulations and protects proprietary information while facilitating robust, collaborative fraud analysis and improving campaign integrity.

🔗 Related Articles

• Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption – https://arxiv.org/abs/2211.06675
• Homomorphic encryption: the future of secure data sharing in finance? – https://www.turing.ac.uk/news/homomorphic-encryption-future-secure-data-sharing-finance
• 2024 Volume 3 Unlocking Data Collaboration Use Cases of Secure Multiparty Computation in Advertising Technology - ISACA – https://www.isaca.org/resources/isaca-journal/issues/2024/volume-3/unlocking-data-collaboration-use-cases-of-secure-multiparty-computation-in-advertising-technology
• What is Homomorphic Encryption? - IBM – https://www.ibm.com/topics/homomorphic-encryption
• 6 Privacy-Enhancing Technologies for AdTech Companies - Clearcode – https://clearcode.cc/blog/privacy-enhancing-technologies-adtech/
• An Introduction to Homomorphic Encryption for Business Leaders | Built In – https://builtin.com/data-science/homomorphic-encryption
• Homomorphic Encryption Explained - Entrust – https://www.entrust.com/blog/2023/11/homomorphic-encryption-explained/