The Rise of Privacy-Enhancing Technologies in 2024

MINAKSHI DEBNATH | DATE: JANUARY 26, 2026

Stuck for ages in a tough spot - choose between using data to spark new ideas or sealing it tight for privacy. Every time, gaining one meant losing the other. Now, maybe, just maybe, that old compromise doesn’t hold weight anymore.

One look at the figures shows something big unfolding. Data released by Market.us reveals that worldwide spending on Privacy-Enhancing Technologies reached about $3.17 billion in 2024; this figure could climb to $28.4 billion within ten years. This isn’t just another minor shift in cybersecurity - instead, it reflects a deep change shaping how digital economies operate across the planet.

The End of the "Privacy-Utility Paradox"

Privacy-enhancing technologies (PETs) are digital solutions that allow information to be collected and processed while maintaining privacy protections. These technologies enable organizations to balance data utility with privacy requirements in several key ways. Why now? It’s the "perfect storm" of maturing mathematical protocols, hardware-level security, and a regulatory supercycle that is making privacy-by-design a legal survival tactic.

Cryptographic Breakthroughs: FHE and ZKPs

For a long time, Fully Homomorphic Encryption (FHE) the ability to compute on encrypted data without ever "unlocking" it was the "holy grail" that was simply too slow for real-world use. That changed this year. Zama, a pioneer in the space, has demonstrated a 100x increase in FHE performance, making it viable for confidential smart contracts and sensitive financial transactions.

Zero-Knowledge Proofs (ZKP) are also seeing explosive growth. Mordor Intelligence reports that ZKPs are growing at a CAGR of 25.71% this year. These allow you to prove something is true like "this user is over 21" without ever seeing the underlying birth date. It’s the ultimate "zero footprint" approach to KYC and AML compliance.

Confidential Computing: Security at the Silicon Level

While math handles the encryption, hardware is providing the "enclaves" where the work gets done. This approach is known as Confidential Computing, and by 2024, the major technology players have fully committed to it.

Apple’s Private Cloud Compute (PCC)

In June 2024, Apple introduced Private Cloud Compute, a platform designed to extend iPhone-level security into the cloud. What stands out isn’t encryption alone it’s the level of transparency built into the model. Apple publishes its software images so independent researchers can verify that the code running in the cloud actually matches their privacy claims. It’s a "non-targetability" model where even Apple’s own admins can’t peek at your data.

Microsoft hasn’t been idle either. At Ignite 2024, they announced "Azure Confidential Clean Rooms." This allows multiple parties to analyze shared data without any single party seeing the raw inputs. More importantly, by integrating NVIDIA H100 GPUs into confidential VMs, Microsoft is enabling "confidential inferencing" for LLMs. This means you can use your most sensitive internal documents to ground your AI (Retrieval-Augmented Generation) without those documents ever being visible to the cloud provider.

Stat Callout: As per Usercentrics the average cost of a data breach reached $4.88 million in 2024, providing a massive financial incentive for the deployment of "zero trust" data architectures.

Industry Deep Dives: BFSI and Healthcare

The sectors with the most to lose are, unsurprisingly, leading the charge.

Banking (BFSI): Accounted for over 30% of the PETs market in 2024. Swift recently piloted an AI fraud shield using Federated Learning across 13 international banks. They trained models on 10 million transactions across borders without ever moving the actual data. The result? Fraud detection was twice as effective as models trained on a single institution’s data.

Healthcare: Synthetic data artificially generated data that mimics real patient statistics is being used to speed up clinical trials. A 2024 study on EHR management confirmed that while there is a "privacy tax" (about a 23.7% computational overhead), the reduction in re-identification risk makes it more than worth it.

The Regulatory Supercycle: From Option to Mandate

If you're operating globally, PETs aren't just a "nice to have" they're becoming a legal requirement. Gartner estimates that modern privacy laws will cover 75% of the world’s population by the end of this year.

When it comes to high-risk AI, the EU’s 2024 rulebook puts privacy tools front and center for cutting down data needs. Over on this side of the Atlantic, rules differ depending on where you stand - state by state. Take Colorado: its new law says builders must act carefully so their algorithms don’t favor one group unfairly. Pulling that off? Nearly out of reach if there's no way to check what happens inside the system - and that’s exactly where these tools step in.

Jurisdiction	Legislation (2024)	Primary Impact on PETs
European Union	EU AI Act	Mandates PETs for high-risk AI training
Colorado	CAIA (SB 24-205)	Disclosures on algorithmic discrimination
California	SB 942	Digital marking/watermarking of AI
Global	ISO/IEC 29100:2024	Standardizes terminology for PETs

The Human Element: Solving the Skills Gap

Here’s the catch the tech is ready, but the people aren't. ISACA reports that technical privacy roles are understaffed in 62% of large organizations. We need a new breed of "full-stack" privacy engineers who understand how to balance a "differential privacy budget" with data accuracy.

At IronQlad, we believe that "Privacy by Design" is evolving into "Compliance as Code." By 2026, the distinction between "security" and "privacy" will likely vanish entirely. AI won't just be a feature; it will be a foundation built on Trusted Execution Environments.

Conclusion

What if companies could work together without exposing private details? Tools like FHE let them pull insights from protected information while staying compliant. Not tomorrow - right now - choices around privacy tech shape who leads and who lags. Waiting for new laws to push change means starting behind. Building skills, using secure computation methods early, sets some apart. Trust becomes real when actions come before mandates. Who moves first might just define what responsible data use looks like later.

KEY TAKEAWAYS

The Market is Exploding: Now picture this - PETs hit 3.17 billion dollars in value during 2024, all because growth kept climbing at nearly 25 percent each year. Speed like that doesn’t come along every decade.

Confidential Computing is Now Standard: Major players like Apple and Microsoft are using hardware-level enclaves to secure AI data "in-use."

Math is Catching Up: FHE and ZKPs have reached the performance thresholds needed for enterprise-scale financial and identity applications.

Compliance is the Catalyst: The EU AI Act and U.S. state laws like Colorado's CAIA are making PETs a legal necessity for high-risk AI.