Real-time bidding (RTB) has transformed digital advertising into a highly efficient and automated system. In milliseconds, advertisers compete for ad placements as users load webpages, ensuring that the most relevant ad appears at just the right time. However, behind this efficiency lies an increasing concern: data privacy.

With regulations such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) putting immense pressure on how personal data is collected, stored, and used, the ad tech industry is at a pivotal point. The need for privacy-preserving technologies has never been more critical.

Enter Homomorphic Encryption (HE)—a cryptographic technique that could radically shift how RTB systems handle user data. By allowing computations on encrypted data without the need to decrypt it, HE has the potential to enable targeted, data-driven advertising without compromising privacy.

This blog delves in-depth into how HE works, its application to RTB, its current benefits and limitations, and why it could become a cornerstone in the future of digital advertising.

What is Homomorphic Encryption?

Homomorphic Encryption is a form of encryption that allows mathematical operations to be performed on ciphertexts. When decrypted, the result of these operations is identical to the outcome if the operations had been performed on the plaintext. In simpler terms, it lets systems process data without ever seeing the raw data.

This property is exceptionally valuable in environments where sensitive data must be used but not exposed, such as digital advertising, healthcare analytics, and finance.

There are three main types of HE:

1. Partially Homomorphic Encryption (PHE):

• Supports only one type of mathematical operation—either addition or multiplication.
• Examples: RSA (multiplicative), Paillier (additive).

2. Somewhat Homomorphic Encryption (SHE):

• It supports a limited number of additions and multiplications before becoming too “noisy” for accurate computation.

3. Fully Homomorphic Encryption (FHE):

• Supports unlimited additions and multiplications.
• Ideal for complex, real-world computations such as RTB logic, machine learning inference, and statistical analysis.

FHE is considered the “holy grail” of privacy-preserving computation, but it comes with high computational costs that challenge its real-time viability—something we will explore in detail.

Why Homomorphic Encryption Matters in RTB

In RTB, user data (such as browsing history, location, and interests) is used by advertisers to determine how much to bid for the opportunity to display a specific ad to a particular user. This data is typically processed in plaintext, raising privacy and compliance concerns.

HE introduces a paradigm shift by allowing this data to remain encrypted throughout the entire process.

Key Benefits of Using HE in RTB:

1. Protecting User Privacy

• Advertisers can compute ad relevance scores and bid prices based on encrypted data, such as demographics and interests, without accessing the raw information.
• Ensures compliance with privacy laws and builds user trust.

2. Encrypted Bid Auctions

• Bids from advertisers can be encrypted.
• The auctioneer (often an ad exchange) can still determine the highest bid and winning advertiser without decrypting any of the bids.

3. Secure Cross-Platform Collaboration

• Different players in the ad tech ecosystem (DSPs, DMPs, SSPs) can collaborate by sharing encrypted data and performing joint computations without exposing their proprietary data.

4. Reduced Data Breach Risk

• Since sensitive information never appears in plaintext, the risk and impact of data breaches are significantly minimised.

5. Compliance with Regulatory Standards

• Encrypted computation aligns with GDPR and CCPA’s requirements to limit access to personal data and reduce the need for consent for certain operations.

6. Preserving Strategic Confidentiality

• Advertisers’ bidding logic and proprietary algorithms remain protected from competitors and intermediaries.

How HE Works in an RTB Ecosystem

User Profile Encryption

User data is encrypted on the client side (i.e., the browser or mobile device) and sent to a data management platform (DMP) or a demand-side platform (DSP). This encrypted data can still be used to:

• Score ad relevance
• Determine eligibility for targeting
• Aggregate campaign metrics

…all without ever being decrypted.

Encrypted Bid Calculation

Advertisers can use encrypted user profiles to calculate encrypted bids using HE-compatible algorithms. For example:

• Bid Score = f(user profile, ad relevance)
• This function operates entirely on encrypted inputs, producing an encrypted output.

Blind Auction Execution

The ad exchange receives encrypted bids from multiple advertisers. It can then use homomorphic comparison operations to determine the highest bid and the winner, all without decrypting the bids.

This maintains bid confidentiality and prevents manipulation or collusion.

Zero-Knowledge Verification

Zero-Knowledge Proofs (ZKPs) can be layered on top of HE to provide mathematical proof that the auction was conducted fairly, without exposing any actual values.

Technical Components and Protocols

Common HE Schemes Used in RTB

HE Scheme	Suitable For	Characteristics
Paillier	PHE (Addition)	Lightweight, supports summation
BFV	SHE/FHE (Exact Arithmetic)	Good for integers
CKKS	FHE (Approximate Arithmetic)	Ideal for real-valued ad scoring
TFHE	FHE (Boolean Circuits)	Fast bitwise operations

Private Histogram Aggregation

Used to generate aggregate audience metrics without identifying individual users:

• User data is split into encrypted shares.
• Each share is processed separately by different parties.
• Aggregated results are homomorphically combined.

Oblivious Bidding

• Advertisers precompute encrypted bids for all user segments.
• When an auction is triggered, the browser retrieves the relevant encrypted bid using Private Information Retrieval (PIR), ensuring the advertiser does not know which segment was targeted.

Homomorphic Comparison

Used by auctioneers to compare encrypted bid values:

• Compute differences between encrypted bids.
• Apply logic to determine the highest bid using homomorphic techniques, such as Boolean gates or encrypted maximum functions.

Real-World Architecture of an HE-Based RTB System

Component	Function	HE Scheme
Encrypted User Vault	Stores encrypted user profiles	TFHE
DSP	Calculates bids using encrypted data	CKKS
Ad Exchange	Compares encrypted bids and picks a winner	BFV/TFHE
Encrypted Logging Layer	Records auction events securely	HE + SHA-256
zk-SNARK Layer	Provides auditability without exposure	HE + ZKPs

Performance Metrics and Limitations

Despite its potential, integrating HE in RTB is not without performance trade-offs.

Latency

• Traditional RTB completes in <100ms.
• Current FHE-based systems average 400–600ms, which can be problematic in real-time environments.

Throughput

• Traditional systems process 50,000+ bids/sec.
• HE-based systems currently max out at 1,500–2,000 bids/sec, though batching and hardware acceleration can help.

Data Size

• HE increases data volume by 10–100×.
• This impacts both network bandwidth and storage requirements.

Emerging Solutions and Optimisations

While the current performance limitations of homomorphic encryption (HE) in real-time ad bidding (RTB) are notable, the research and development community is actively working on solutions to address these limitations. These innovations aim to make HE not only feasible but also practical for commercial-scale deployment in digital advertising ecosystems.

1. Hybrid HE Models

One of the most promising developments is the hybrid approach to encryption. Instead of relying solely on fully homomorphic encryption (FHE), platforms are increasingly combining it with:

• Partially Homomorphic Encryption (PHE) for operations like addition (e.g., aggregating impressions or clicks).

• Somewhat Homomorphic Encryption (SHE) for moderately complex bid computations.

• FHE is reserved only for final-stage processing, such as secure comparisons.

This selective application reduces overhead while preserving privacy.

2. Hardware Acceleration

Processing encrypted data is computationally intensive. To overcome this, researchers and tech firms are investing in hardware solutions:

• Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are being optimised to handle HE operations.

• Specialised HE Co-Processors are under development, aiming to provide ASIC-level performance for encryption workloads.

Recent benchmarks have shown that GPU acceleration can offer a 4× to 6× speed improvement in encrypted bidding operations.

3. Cryptosystem Optimisation

Certain lattice-based cryptosystems, like CKKS, have been tailored for RTB use cases:

• CKKS supports approximate real number arithmetic, which aligns well with bid scoring systems.

• The TFHE scheme enables fast encrypted Boolean comparisons, which are critical for bid evaluations.

Combined, these systems allow for encrypted workflows that are both efficient and accurate enough for many real-world ad tech applications.

Security-Privacy Trade-offs and Mitigation Strategies

While homomorphic encryption boosts privacy, it’s not without risks. Understanding these trade-offs is essential for secure deployment.

1. Information Leakage

• Repeated Winner Pattern: If the same advertiser continually wins auctions, it may leak user segment information even if the bids are encrypted.

• Ciphertext Structure: Large ciphertexts may increase exposure to traffic analysis and side-channel attacks.

Mitigation:

• Apply differential privacy by adding controlled noise to the results.
• Use batching techniques to anonymise individual transactions.
• Implement multi-party computation (MPC) to further obscure sensitive bid logic.

2. Computational Error

• CKKS introduces approximation errors due to the use of floating-point operations.
• Errors are typically within ±0.1%, which is acceptable in most ad scoring scenarios but must be accounted for in systems requiring precision.

Mitigation:

• Use error-bounded thresholds for bid comparisons.
• Calibrate scoring models with margin-of-error tolerances.

3. Key Management

Managing encryption keys across multiple platforms and participants can be complex:

• Each party must securely store and manage its key pair.
• Data processed across domains requires careful orchestration to prevent unauthorised access.

Mitigation:

• Employ threshold cryptography: decryption only occurs if a majority (e.g., 3 of 5 parties) agree.
• Utilise hardware security modules (HSMs) for secure key storage.

Economic and Infrastructure Considerations

Integrating HE into RTB is not only a technical challenge but also an economic one. The overhead can be substantial, both in terms of compute cost and engineering complexity.

Estimated Costs Compared to Traditional RTB

Metric	Traditional RTB	HE-Based RTB
Latency (avg/auction)	90ms	550ms
Throughput	50,000 bids/sec	1,800 bids/sec
Infrastructure cost	Baseline	5× – 10× increase
Data transmission volume	1×	10× – 100×

However, this cost must be weighed against:

• Regulatory penalties for non-compliance.
• Consumer trust and brand value from privacy-forward strategies.
• Potential fines under GDPR or CCPA could be far more damaging than the cost of an encrypted infrastructure.

Commercial Feasibility Timeline

Industry projections suggest:

• 2025–2026: Viable HE-based pilots and production tools in low-volume ad exchanges.
• 2026–2028: Achieve latency parity with traditional RTB using HE and specialised hardware.
• Beyond 2028: Wide-scale adoption as part of privacy-first advertising architectures.

Integration with Other Privacy-Enhancing Technologies (PETs)

HE isn’t an isolated solution—it works best in combination with other Privacy-Enhancing Technologies (PETs) to form a complete privacy architecture.

1. Secure Multi-Party Computation (MPC)

MPC allows different parties to compute a joint function over their inputs without revealing the inputs to each other. When combined with HE, MPC can:

• Support distributed encrypted auctions.
• Eliminate the need for centralised control.
• Reduce trust assumptions between competitors (e.g., rival DSPs).

2. Federated Learning

With federated learning, machine learning models are trained directly on user devices. The model weights are updated and shared, not the raw data.

When integrated with HE:

• Model updates are encrypted during transfer.
• Only the aggregator decrypts the results to update the global model.

This architecture enables encrypted click-through rate (CTR) prediction models and other optimisations without user-level data ever leaving the device in clear form.

3. Differential Privacy

Adding noise to computations ensures that no single user’s data significantly influences an output, making re-identification nearly impossible.

When combined with HE:

• Even encrypted results provide an additional layer of privacy protection.
• Noise addition can happen before or after homomorphic processing.

Use Case: A Fully Encrypted Ad Auction

Let’s walk through what a real-world HE-powered RTB transaction might look like:

1. User Visits Web Page

• The browser sends an encrypted user profile to the publisher’s ad server.

2. DSPs Receive Encrypted Data

• Each Demand-Side Platform uses this data to compute encrypted bid prices using homomorphic algorithms.

3. Ad Exchange Collects Bids

• The exchange compares encrypted bids using HE-based comparison protocols.

4. Winner is Selected

• The highest encrypted bid is identified without revealing bid values.

5. Encrypted Logs are Stored

• The entire transaction is recorded in a tamper-proof, encrypted log for compliance and auditing.

6. Optional: Zero-Knowledge Proof

• A zk-SNARK is generated to prove the auction was fair, without revealing any participant data.

Future Research Directions

The field of HE in RTB is rapidly evolving. Key areas of future exploration include:

1. Adaptive HE Systems

• Dynamically switch between PHE, SHE, and FHE based on the complexity of the task.
• Optimise resource usage while maintaining security.

2. Homomorphic Machine Learning

• Train and run predictive models (such as CTR, conversion rate, and lifetime value) on encrypted datasets.
• Preserve user privacy during the entire machine learning pipeline.

3. Post-Quantum Cryptography

• Transition to quantum-resistant encryption standards.
• Utilise NIST-approved algorithms, such as Kyber and Dilithium, in conjunction with HE for enhanced long-term security.

4. Cross-Chain Privacy Auctions

• Enable decentralised auctions across different ad networks or blockchain platforms using HE + Inter-Blockchain Communication (IBC) protocols.

Final Thoughts: A Privacy-Preserving Path Forward

Homomorphic encryption offers a revolutionary path forward for real-time ad bidding. It strikes a rare balance, allowing advertisers to make data-driven decisions while fully respecting user privacy.

While the road to widespread adoption is paved with technical and economic challenges, the benefits of compliance, trust, data security, and ethical advertising are profound.

With the development of faster cryptographic libraries, hardware accelerators, and hybrid PETs, HE is rapidly transitioning from theoretical promise to practical implementation.

The future of advertising isn’t just targeted; it’s encrypted, ethical, and privacy-first.