Entries Tagged "cryptography"

Page 1 of 55

The Intersection of Encryption and AI

As part of their 20th Anniversary celebration, Dark Reading asked five cybersecurity industry leaders who wrote blogs or columns for them over the years to select their favorite piece and share their reflections on the topic today. This is my section.

Renowned technologist and author Bruce Schneier contributed a column on June 20, 2010, warning about cryptography’s inability to secure modern networks, a point he says he has been trying to argue since 2000.

“For a while now, I’ve pointed out that cryptography is singularly ill-suited to solve the major network security problems of today: denial-of-service attacks, website defacement, theft of credit card numbers, identity theft, viruses and worms, DNS attacks, network penetration, and so on.

“Recently, I talked to a former NSA employee at a conference. He told me that back in the 1990s, he had a copy of my book Applied Cryptography by his desk, as did many other cryptographers working at Ft. Meade. People were allowed to refer to it, but they were not allowed to cite it.

“The 1990s were an important decade for cryptography. This was before the internet went mass market, when cryptography was just emerging from a niche academic discipline to a mainstream engineering one. There wasn’t much that programmers could read. The NSA used my book for the same reason it became a bestseller: because it collected all the academic cryptography of the time in one place and made it understandable to people who weren’t mathematicians. They feared it for exactly the same reason.

“I’ve been thinking about that conversation as I revisit a 2010 essay I wrote for Dark Reading, ‘The Failure of Cryptography to Secure Modern Networks.’ Cryptography has inherent mathematical properties that greatly favor the defender. Adding a single bit to the length of a key adds only a slight amount of work for the defender but doubles the amount of work the attacker has to do. Doubling the key length doubles the amount of work the defender has to do (if that—I’m being approximate here) but increases the attacker’s workload exponentially. For many years, we have exploited that mathematical imbalance.

“Computer security is much more balanced. There’ll be a new attack, and a new defense, and a new attack, and a new defense. It’s an arms race between attacker and defender. And it’s a very fast arms race. New vulnerabilities are discovered all the time. The balance can tip from defender to attacker overnight, and back again the night after. Computer security defenses are inherently very fragile.

“That isn’t a new idea. I said much the same thing in the preface to my 2000 book, Secrets and Lies:

“‘Cryptography is a branch of mathematics. And like all mathematics, it involves numbers, equations, and logic. Security, real security that you or I might find useful in our lives, involves people: things people know, relationships between people, people and how they relate to machines. Digital security involves computers: complex, unstable, buggy computers.’

“I especially like how I phrased it in 2016: ‘Cryptography is harder than it looks, primarily because it looks like math. Both algorithms and protocols can be precisely defined and analyzed. This isn’t easy, and there’s a lot of insecure crypto out there, but we cryptographers have gotten pretty good at getting this part right. However, math has no agency; it can’t actually secure anything. For cryptography to work, it needs to be written in software, embedded in a larger software system, managed by an operating system, run on hardware, connected to a network, and configured and operated by users. Each of these steps brings with it difficulties and vulnerabilities.’

“It’s a lesson we have all learned over the decades. Cryptography is still necessary for cybersecurity—although I wouldn’t have used that word back then—but is not sufficient. There are particular attack and forms of mass surveillance that cryptography prevents. But as computers have infused throughout our lives, and networks have connected all those computers, those aspects of cybersecurity have become increasingly important, and vulnerable.

“Today, the cybersecurity world is changing yet again, this time due to the capabilities of artificial intelligence. AI isn’t advancing cryptography, but it’s changing cybersecurity. AI has demonstrated a superhuman ability to find vulnerabilities in software and to write exploits. A similar ability to write patches is probably coming. This has profound implications for both attackers and defenders, and it is unclear who will win the particular arms race in a world of what I call instant software.”

Posted on June 2, 2026 at 7:06 AM • View Comments

Book Review: The Business of Secrets

The Business of Secrets: Adventures in Selling Encryption Around the World by Fred Kinch (May 24, 2024)

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.

Datotek sold cryptographic equipment in the era after rotor machines and before modern academic cryptography. The company initially marketed computer-file encryption, but pivoted to link encryption—low-speed data, voice, fax—because that’s what the market wanted.

These were the years where the NSA hired anyone promising in the field, and routinely classified—and thereby blocked—publication of academic mathematics papers of those they didn’t hire. They controlled the fielding of strong cryptography by aggressively using the International Traffic in Arms regulation. Kinch talks about the difficulties in getting an expert license for Datotek’s products; he didn’t know that the only reason he ever got that license was because the NSA was able to break his company’s stuff. He had no idea that his largest competitor, the Swiss company Crypto AG, was owned and controlled by the CIA and its West German equivalent. “Wouldn’t that have made our life easier if we had known that back in the 1970s?” Yes, it would. But no one knew.

Glimmers of the clandestine world peek out of the book. Countries like France ask detailed tech questions, borrow or buy a couple of units for “evaluation,” and then disappear again. Did they break the encryption? Did they just want to see what their adversaries were using? No one at Datotek knew.

Kinch “carried the key generator logic diagrams and schematics” with him—even today, it’s good practice not to rely on their secrecy for security—but the details seem laughably insecure: four linear shift registers of 29, 23, 13, and 7 bits, variable stepping, and a small nonlinear final transformation. The NSA probably used this as a challenge to its new hires. But Datotek didn’t know that, at the time.

Kinch writes: “The strength of the cryptography had to be accepted on trust and only on trust.” Yes, but it’s so, so weird to read about it in practice. Kinch demonstrated the security of his telephone encryptors by hooking a pair of them up and having people listen to the encrypted voice. It’s rather like demonstrating the safety of a food additive by showing that someone doesn’t immediately fall over dead after eating it. (In one absolutely bizarre anecdote, an Argentine sergeant with a “hearing defect” could understand the scrambled analog voice. Datotek fixed its security, but only offered the upgrade to the Argentines, because no one else complained. As I said, no one knew anything.)

In his postscript, he writes that even if the NSA could break Datotek’s products, they were “vastly superior to what [his customers] had used previously.” Given that the previous devices were electromechanical rotor machines, and that his primary competition was a CIA-run operation, he’s probably right. But even today, we know nothing about any other country’s cryptanalytic capabilities during those decades.

A lot of this book has a “you had to be there” vibe. And it’s mostly tone-deaf. There is no real acknowledgment of the human-rights-abusing countries on Datotek’s customer list, and how their products might have assisted those governments. But it’s a fascinating artifact of an era before commercial cryptography went mainstream, before academic cryptography became approved for US classified data, before those of us outside the triple fences of the NSA understood the mathematics of cryptography.

This book review originally appeared in AFIO.

Posted on November 13, 2025 at 7:09 AM • View Comments

Signal’s Post-Quantum Cryptographic Implementation

Signal has just rolled out its quantum-safe cryptographic implementation.

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.

The Signal engineers have given this third ratchet the formal name: Sparse Post Quantum Ratchet, or SPQR for short. The third ratchet was designed in collaboration with PQShield, AIST, and New York University. The developers presented the erasure-code-based chunking and the high-level Triple Ratchet design at the Eurocrypt 2025 conference. At the Usenix 25 conference, they discussed the six options they considered for adding quantum-safe forward secrecy and post-compromise security and why SPQR and one other stood out. Presentations at the NIST PQC Standardization Conference and the Cryptographic Applications Workshop explain the details of chunking, the design challenges, and how the protocol had to be adapted to use the standardized ML-KEM.

Jacomme further observed:

The final thing interesting for the triple ratchet is that it nicely combines the best of both worlds. Between two users, you have a classical DH-based ratchet going on one side, and fully independently, a KEM-based ratchet is going on. Then, whenever you need to encrypt something, you get a key from both, and mix it up to get the actual encryption key. So, even if one ratchet is fully broken, be it because there is now a quantum computer, or because somebody manages to break either elliptic curves or ML-KEM, or because the implementation of one is flawed, or…, the Signal message will still be protected by the second ratchet. In a sense, this update can be seen, of course simplifying, as doubling the security of the ratchet part of Signal, and is a cool thing even for people that don’t care about quantum computers.

Also read this post on X.

Posted on October 29, 2025 at 7:09 AM • View Comments

NCSC Guidance on “Advanced Cryptography”

The UK’s National Cyber Security Centre just released its white paper on “Advanced Cryptography,” which it defines as “cryptographic techniques for processing encrypted data, providing enhanced functionality over and above that provided by traditional cryptography.” It includes things like homomorphic encryption, attribute-based encryption, zero-knowledge proofs, and secure multiparty computation.

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.

And:

In almost all cases, it is bad practice for users to design and/or implement their own cryptography; this applies to Advanced Cryptography even more than traditional cryptography because of the complexity of the algorithms. It also applies to writing your own application based on a cryptographic library that implements the Advanced Cryptography primitive operations, because subtle flaws in how they are used can lead to serious security weaknesses.

The conclusion:

Advanced Cryptography covers a range of techniques for protecting sensitive data at rest, in transit and in use. These techniques enable novel applications with different trust relationships between the parties, as compared to traditional cryptographic methods for encryption and authentication.

However, there are a number of factors to consider before deploying a solution based on Advanced Cryptography, including the relative immaturity of the techniques and their implementations, significant computational burdens and slow response times, and the risk of opening up additional cyber attack vectors.

There are initiatives underway to standardise some forms of Advanced Cryptography, and the efficiency of implementations is continually improving. While many data processing problems can be solved with traditional cryptography (which will usually lead to a simpler, lower-cost and more mature solution) for those that cannot, Advanced Cryptography techniques could in the future enable innovative ways of deriving benefit from large shared datasets, without compromising individuals’ privacy.

NCSC blog entry.

Posted on May 2, 2025 at 7:03 AM • View Comments

AIs as Trusted Third Parties

This is a truly fascinating paper: “Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography.” The basic idea is that AIs can act as trusted third parties:

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.

When I was writing Applied Cryptography way back in 1993, I talked about human trusted third parties (TTPs). This research postulates that someday AIs could fulfill the role of a human TTP, with added benefits like (1) being able to audit their processing, and (2) being able to delete it and erase their knowledge when their work is done. And the possibilities are vast.

Here’s a TTP problem. Alice and Bob want to know whose income is greater, but don’t want to reveal their income to the other. (Assume that both Alice and Bob want the true answer, so neither has an incentive to lie.) A human TTP can solve that easily: Alice and Bob whisper their income to the TTP, who announces the answer. But now the human knows the data. There are cryptographic protocols that can solve this. But we can easily imagine more complicated questions that cryptography can’t solve. “Which of these two novel manuscripts has more sex scenes?” “Which of these two business plans is a riskier investment?” If Alice and Bob can agree on an AI model they both trust, they can feed the model the data, ask the question, get the answer, and then delete the model afterwards. And it’s reasonable for Alice and Bob to trust a model with questions like this. They can take the model into their own lab and test it a gazillion times until they are satisfied that it is fair, accurate, or whatever other properties they want.

The paper contains several examples where an AI TTP provides real value. This is still mostly science fiction today, but it’s a fascinating thought experiment.

Posted on March 28, 2025 at 7:01 AM • View Comments

Implementing Cryptography in AI Systems

Interesting research: “How to Securely Implement Cryptography in Deep Neural Networks.”

Abstract: The wide adoption of deep neural networks (DNNs) raises the question of how can we equip them with a desired cryptographic functionality (e.g, to decrypt an encrypted input, to verify that this input is authorized, or to hide a secure watermark in the output). The problem is that cryptographic primitives are typically designed to run on digital computers that use Boolean gates to map sequences of bits to sequences of bits, whereas DNNs are a special type of analog computer that uses linear mappings and ReLUs to map vectors of real numbers to vectors of real numbers. This discrepancy between the discrete and continuous computational models raises the question of what is the best way to implement standard cryptographic primitives as DNNs, and whether DNN implementations of secure cryptosystems remain secure in the new setting, in which an attacker can ask the DNN to process a message whose “bits” are arbitrary real numbers.

In this paper we lay the foundations of this new theory, defining the meaning of correctness and security for implementations of cryptographic primitives as ReLU-based DNNs. We then show that the natural implementations of block ciphers as DNNs can be broken in linear time by using such nonstandard inputs. We tested our attack in the case of full round AES-128, and had success rate in finding randomly chosen keys. Finally, we develop a new method for implementing any desired cryptographic functionality as a standard ReLU-based DNN in a provably secure and correct way. Our protective technique has very low overhead (a constant number of additional layers and a linear number of additional neurons), and is completely practical.

Posted on February 21, 2025 at 10:33 AM • View Comments

1 2 3 … 55

Sidebar photo of Bruce Schneier by Joe MacInnis.