Quantum Resistant Encryption

The performance gains in quantum computing are so significant that Shor's algorithm will break our current encryption methods.

Organizations worldwide face two challenges

1.  "Harvest now and encrypt later."

2. " Trust now, forget later."

The former poses a threat to our current encrypted data lakes. Every time adversaries harvest this encrypted data, they can later apply quantum-based strategies to decrypt it. This will compromise confidentiality.

The second threat is that malicious actors already collect insights about what they will exploit when the capabilities arrive. This will compromise integrity.

Homomorphic encryption (HE) has the potential to transform the way sensitive data is processed, but the path from theoretical breakthroughs to real-world adoption is fraught with unexpected obstacles.

Challenge

We live in a digitized world, and cryptography has underpinned these services for decades. Nothing like that would have been doable without digital signatures and public-key-based encryption. This layer of security and privacy comes with a price. When we increase the former and the latter by using FHE (Fully Homomorphic Encryption) schemes, we can measure their impact on processing duration. In highly sensitive domains, this security overhead might be acceptable, but regular users often trade security for convenience. In the worst case, they avoid additional security measures because they're time-consuming and create a negative user experience.

Causes

After all, security and privacy are hard to measure. Often, it is only after a significant attack and data exfiltration that victims understand the value of cybersecurity. These insights come quite late. The question is: how should we bring this end-game thinking to everyone who consumes digitized products? Would they pay more if they understood that there is a better level of security and privacy? From a technical perspective, in FHE (Fully Homomorphic Encryption) schemes, we have an excellent solution for sharing or processing sensitive data, as operations are performed on the encrypted data. However, this comes at a cost in terms of increased processing time and resource utilization. Service costs for products utilizing more secure FHE schemes would increase, potentially resulting in a lower user experience.

Proposal to address this challenge

Many goods and services are rated these days. For instance, the stock market was largely unregulated in its early days, resulting in millions of losses due to defaults. After introducing the rating of Stox, consumers made more thoughtful decisions. To protect consumers, we could introduce cyber ratings for digitized products to showcase their security levels. Highly rated products could be a differentiator, allowing their products to charge more and invest this additional income in specialized libraries or hardware to support FHE schemes.

Practical Viability vs. Theoretical Promise

The idea behind FHE remains groundbreaking because it allows operations on encrypted ciphertext, thereby preserving privacy and enabling zero-knowledge proofs. Besides its outstanding security properties, some unresolved challenges remain. Large ciphertext sizes create a processing overhead, and bootstrapping increases complexity and processing times. Essential steps towards the practical adoption of FHE include introducing libraries, which reduce the entry barrier and make these concepts accessible to developers. In low-latency domains, such as banking, these FHE implementations are still not widely accepted; however, as computing speed increases, the privacy advantages may soon outweigh their disadvantages.

#QuantumComputing #Security #Encyrption