**Privacy-Preserving Machine Learning (PPML) technologies — Homomorphic Encryption (FHE), Federated Learning (FL), Secure Multi-Party Computation (SMPC) — face a fundamental product strategy choice: pursue Product-Market Fit (universal Plug & Play software) or Project-Market Fit (custom per-client implementations).** This article argues most PPML methods cannot yet be Plug & Play due to required cryptographic expertise, complex configurations, and integration challenges. The strategic conclusion: PETs are actually "Partnership Enhancing Technologies" — collaboration between specialized vendors creates better outcomes than any single-vendor universal solution.
When you embark on research and development in cutting-edge fields like Privacy-Preserving Machine Learning, do you have a clear vision of the final outcome?
Will your product be a software solution that seamlessly integrates into any platform, offering a Plug & Play experience? Or will it be a service requiring significant customization and implementation tailored to the needs of each client?
These are not merely rhetorical questions; they are crucial considerations that should be addressed during the early stages of company strategy development, well before seeking investment.
Is it even possible to predict whether your development will become a universal product or remain a project-based service? And if so, how important is this prediction?
The difference between these two approaches extends beyond technical implementation; it influences the direction of your company's growth, your target audience, and the long-term outcomes you can expect.
It's clear that being a product-based company is fundamentally different from being a custom development company. Beyond the differences in business models, these companies have distinct structures, teams with different mindsets, and varying life cycles. For instance, a product-based company focuses on scalability, automation, and minimizing customization, while custom development is geared toward flexibility, client-specific adaptations, and tailored solutions.
These differences also extend to the types of investors you can attract. A product that offers easy integration (Plug & Play) holds significant potential for exponential growth, making it appealing to venture capitalists who are willing to take risks for high returns. On the other hand, investors interested in dividend-focused stories, typical of project-based companies, anticipate a more linear growth in revenue and expenses. They are generally less risk-tolerant but value stability and predictability.
When it comes to Privacy-Preserving Machine Learning and other complex technologies, the choice between Product-Market Fit and Project-Market Fit becomes particularly crucial. Striving for Product-Market Fit means you're developing a universal solution ready for scaling and widespread adoption. In contrast, pursuing Project-Market Fit focuses on specialized solutions that require significant customization for each client. Each approach has its own advantages and disadvantages, and the choice between them will ultimately shape the direction your company takes.
The characteristics of Plug & Play software can be broadly divided into two categories based on the influence of the development team.
The first category is more heavily influenced by the skill and expertise of the development team.
The second category depends more on the latest scientific and technological advancements, standardization, and the fundamental limitations imposed by the laws of science.
The First Category of Characteristics
User-Friendly Interface: If user interaction is required, the interface should be intuitive and easy to use, guiding the user through all necessary steps with clear instructions and helpful feedback. It should also require minimal training.
Scalability: The software should be able to handle the addition of numerous new components over time without degrading performance or compromising the user experience. Automatic updates should be available to improve performance, enhance security, and add new features.
Security: The software must adhere to best security practices to protect user data.
Dynamic Resource Allocation: The software should be capable of dynamically allocating and managing system resources (such as memory, I/O ports, and IRQs) as new components are added or removed.
Error Handling and Recovery: The software should have robust mechanisms for detecting and handling errors, conflicts, and other issues that may arise during the configuration and operation of new components. Additionally, it should be capable of restoring functionality without requiring significant user intervention.
The Second Category of Characteristics
Automatic Configuration: The ability to automatically detect and configure new hardware or software components without requiring manual intervention from the user.
Minimal User Interaction: Users should not need to perform complex configuration tasks. Installation and initialization should be automatic or straightforward, often requiring only a few simple steps or none at all.
Compatibility and Standardization: Adherence to industry standards and protocols to ensure seamless operation with a wide range of hardware and other software systems. The software should automatically detect and install necessary dependencies or components.
Seamless Integration: New components should integrate smoothly with existing system components, maintaining system stability and performance. The software should provide core functionalities without requiring additional components or purchases.
Let's evaluate how some of the most commonly mentioned Privacy-Preserving Machine Learning techniques align with the characteristics of the second category.
| Signs of Plug & Play Software | Homomorphic encryption (computations on encrypted data without decrypting it) | Federated Learning (trains machine learning models across decentralized devices without sharing raw data) | Secure Multi-Party Computation (multiple parties jointly computing a function over their inputs while keeping those inputs private) |
|---|---|---|---|
| Automatic Configuration | Often requires custom implementation for specific use cases and hardware optimization | Frequently necessitates bespoke configurations for communication protocols, aggregation, and security, which vary significantly across different applications | Requires tailored protocols depending on the number of parties and specific computational tasks, making it challenging to generalize |
| Minimal User Interaction | Require a deep understanding of cryptography and complex configurations | Network conditions, data heterogeneity, and system architecture can significantly impact performance and convergence, making it challenging to achieve a truly seamless experience | Demand extensive knowledge of cryptography and intricate setup processes |
| Compatibility and Standardization | In 2018, the Homomorphic Encryption Consortium developed the Homomorphic Encryption Standard, outlining security requirements for Fully Homomorphic Encryption (FHE) applications | It necessitates coordination between devices, management of model updates, and ensuring data privacy | ISO/IEC 4922-2:2024, titled “Information Security — Secure Multiparty Computation, Part 2: Mechanisms Based on Secret Sharing,” provides a framework for these mechanisms |
| Seamless Integration | Requires specialized encryption schemes and operations on encrypted data, which are computationally intensive and difficult to integrate into existing systems | Demands coordination among numerous decentralized clients and secure aggregation of their updates, often involving complex protocols and infrastructure | Involves performing computations across multiple parties without revealing their original data, necessitating intricate protocols and careful coordination |
Do these characteristics pose obstacles to creating Plug & Play Software? Do you agree with this assessment?
In cases familiar to Guardora, the mentioned methods and protocols are often combined with additional Privacy-Enhancing Technologies, such as:
- Synthetic Data
- Differential Privacy
- Trusted Execution Environments
- Zero-Knowledge Proofs
- Swarm Learning
Can a single vendor provide the best solutions based on each of these approaches? Will they all be Plug & Play Software, or will they require custom development?
In the context of user interactions, these are referred to as 'Partnership Enhancing Technologies' or even 'Trust Technologies.' This is no surprise, as trust is fundamental to all of these solutions.
However, enhancing partnerships is crucial not only for users but also for developers working with various methods and protocols.
Let’s look at this from a practical perspective:
- Each excels in its own area: It's impossible to be an expert in all aspects of Privacy Enhancing Technologies. Every developer and company has its own strengths, and that’s perfectly fine. What’s important is finding common ground and sharing expertise.
- Use cases require combination: To ensure comprehensive data protection and privacy, it’s often necessary to combine multiple methods and protocols. Different technologies can complement each other, enhancing the overall outcome and providing users with maximum protection.
- Not everything can be Plug & Play: Despite our best efforts to create universal solutions, the reality is that not all methods can be implemented in a Plug & Play format. Some elements will inevitably require customization to meet specific needs and tasks. In this context, collaboration among developers becomes key to success.
What are your thoughts on this? Are you as open to collaboration as Guardora?
We believe that meaningful progress in Privacy Enhancing Technologies can only be achieved through joint efforts. Join our community on Discord, and let’s discuss how we can bring together our innovative and valuable solutions.
Frequently Asked Questions
Can PPML solutions be Plug & Play?
Not yet, in most cases. The article evaluates three core PPML primitives — Homomorphic Encryption (FHE), Federated Learning (FL), Secure Multi-Party Computation (SMPC) — against four Plug & Play characteristics: Automatic Configuration, Minimal User Interaction, Compatibility/Standardization, Seamless Integration. All three primitives fail on most criteria. FHE and SMPC require deep cryptographic expertise. FL requires bespoke configurations for communication protocols, aggregation, and security that vary across applications. While some standards exist (Homomorphic Encryption Standard 2018, ISO/IEC 4922-2:2024 for SMPC), seamless integration into existing systems remains elusive. **Conclusion:** specialized vendors and partnerships are more practical than universal Plug & Play software.
What's the difference between Product-Market Fit and Project-Market Fit for PPML?
**Product-Market Fit** means developing a universal solution ready for scaling and widespread adoption — Plug & Play software with automatic configuration, minimal user interaction, and standard integration. **Project-Market Fit** means specialized solutions requiring significant customization per client. The two strategies imply fundamentally different companies: product-based focuses on scalability, automation, and minimizing customization; custom development is geared toward flexibility and client-specific solutions. They attract different investors (VCs for Product fit; dividend-focused investors for Project fit), require different team mindsets, and produce different life cycles. For PPML specifically, technical realities (cryptographic complexity, performance variability) often force Project-Market Fit.
Why is FHE not Plug & Play?
Fully Homomorphic Encryption faces three Plug & Play obstacles. **(1) Custom implementation per use case:** FHE schemes (BGV, BFV, CKKS) have different performance characteristics for different workloads. Hardware optimization is task-specific. **(2) Deep cryptographic expertise required:** users must understand circuit design, noise budgets, bootstrapping operations. **(3) Computational intensity:** encrypted operations are slow; specialized hardware (FPGA, ASIC accelerators) creates integration complexity. Despite the **Homomorphic Encryption Standard 2018** providing security framework, real-world FHE deployments remain expert-intensive bespoke implementations.
Why is Federated Learning not Plug & Play?
Federated Learning faces unique obstacles. **(1) Bespoke configurations for communication protocols, aggregation strategies, security mechanisms** — these vary across applications. **(2) Network conditions, data heterogeneity, system architecture** significantly impact performance and convergence — making truly seamless experience difficult. **(3) Coordination required** between devices, model update management, and data privacy assurance. Most FL deployments require ML engineers to design custom training loops, encryption layers, and drift detection. While frameworks like Flower and NVIDIA FLARE provide building blocks, they're toolkits requiring expertise, not Plug & Play products.
Why is Secure Multi-Party Computation not Plug & Play?
SMPC requires tailored protocols depending on number of parties and specific computational tasks — making generalization challenging. Deep cryptographic expertise is required for setup. Even with **ISO/IEC 4922-2:2024** ("Information Security — Secure Multiparty Computation, Part 2: Mechanisms Based on Secret Sharing") providing standardization framework, real deployments require careful coordination of intricate protocols. SMPC is the most coordination-intensive of the three primitives — Plug & Play simplicity is fundamentally at odds with multi-party cryptographic security guarantees.
What is the Homomorphic Encryption Standard?
In 2018, the **Homomorphic Encryption Consortium** developed the **Homomorphic Encryption Standard**, outlining security requirements for Fully Homomorphic Encryption (FHE) applications. The standard provides a security framework but does not prescribe specific implementation details — leaving substantial room for vendor variation. It addresses concerns like security parameter selection, scheme variants (BGV, BFV, CKKS), and threat models. The standard's existence enables some baseline FHE interoperability but does not solve the broader Plug & Play challenge — FHE deployments still require expert customization per use case.
What is ISO/IEC 4922-2:2024?
**ISO/IEC 4922-2:2024** is "Information Security — Secure Multiparty Computation, Part 2: Mechanisms Based on Secret Sharing" — providing an international standard framework for SMPC implementations. The standard addresses one of the major SMPC protocol families (secret-sharing-based, as opposed to garbled-circuit or homomorphic-encryption-based). Its existence is a positive signal for SMPC maturity, but like the Homomorphic Encryption Standard, it doesn't eliminate the need for expert implementation. ISO/IEC 4922 helps with interoperability across implementations conforming to it, but Plug & Play remains aspirational.
What are Partnership Enhancing Technologies (PETs)?
In the article's framing, "PET" traditionally refers to **Privacy-Enhancing Technologies** (Differential Privacy, FHE, FL, SMPC, ZKP, TEE, etc.). The article reframes PETs as **"Partnership Enhancing Technologies"** — arguing trust between users, between data parties, AND **between vendors** is fundamental to PPML adoption. Each PPML vendor specializes in different methods; no single vendor masters all of FHE, FL, SMPC, DP, TEE, ZKP, Synthetic Data, Swarm Learning simultaneously. Most real PPML deployments combine multiple methods from multiple vendors. **Strategic conclusion:** collaboration enables comprehensive outcomes that individual vendors cannot achieve alone.
