From the vantage point of today, it’s surreal reading about the commercial cryptography business in the 1970s. Nobody knew anything. The manufacturers didn’t know whether the cryptography they sold was any good. The customers didn’t know whether the crypto they bought was any good. Everyone pretended to know, thought they knew, or knew better than to even try to know.

The Business of Secrets is the self-published memoirs of Fred Kinch. He was founder and vice president of—mostly sales—at a US cryptographic hardware company called Datotek, from company’s founding in 1969 until 1982. It’s mostly a disjointed collection of stories about the difficulties of selling to governments worldwide, along with descriptions of the highs and (mostly) lows of foreign airlines, foreign hotels, and foreign travel in general. But it’s also about encryption...

Ars Technica has a really good article with details:

Ultimately, the architects settled on a creative solution. Rather than bolt KEM onto the existing double ratchet, they allowed it to remain more or less the same as it had been. Then they used the new quantum-safe ratchet to implement a parallel secure messaging system.

Now, when the protocol encrypts a message, it sources encryption keys from both the classic Double Ratchet and the new ratchet. It then mixes the two keys together (using a cryptographic key derivation function) to get a new encryption key that has all of the security of the classical Double Ratchet but now has quantum security, too...

It’s full of good advice. I especially appreciate this warning:

When deciding whether to use Advanced Cryptography, start with a clear articulation of the problem, and use that to guide the development of an appropriate solution. That is, you should not start with an Advanced Cryptography technique, and then attempt to fit the functionality it provides to the problem. ...

Abstract: We often interact with untrusted parties. Prioritization of privacy can limit the effectiveness of these interactions, as achieving certain goals necessitates sharing private data. Traditionally, addressing this challenge has involved either seeking trusted intermediaries or constructing cryptographic protocols that restrict how much data is revealed, such as multi-party computations or zero-knowledge proofs. While significant advances have been made in scaling cryptographic approaches, they remain limited in terms of the size and complexity of applications they can be used for. In this paper, we argue that capable machine learning models can fulfill the role of a trusted third party, thus enabling secure computations for applications that were previously infeasible. In particular, we describe Trusted Capable Model Environments (TCMEs) as an alternative approach for scaling secure computation, where capable machine learning model(s) interact under input/output constraints, with explicit information flow control and explicit statelessness. This approach aims to achieve a balance between privacy and computational efficiency, enabling private inference where classical cryptographic solutions are currently infeasible. We describe a number of use cases that are enabled by TCME, and show that even some simple classic cryptographic problems can already be solved with TCME. Finally, we outline current limitations and discuss the path forward in implementing them...