AICrypt 2024 | EUROCRYPT

About AICrypt

Recently, the synergy between artificial intelligence (AI) and security has gained increasing prominence and significance. This evolution naturally arises from the need to enhance security with greater efficiency. Among the many areas of security benefiting from AI's integration, cryptography stands as a notable field. We are already witnessing the application of AI techniques to address several problems in cryptography, such as enhancing defenses against implementation attacks and hardware Trojans, and investigating attacks on Physical Unclonable Functions (PUFs). Beyond AI's contributions to cryptography, it is also possible to identify the use of cryptography to solve security and privacy issues in AI systems as an emerging and pivotal subject. The mounting frequency of AI system attacks urges us to explore potential research avenues involving cryptographic strategies to counteract these threats. Our objective is to convene experts from both academic and industrial backgrounds, each contributing to diverse facets of cryptography and AI, to facilitate knowledge exchange and foster collaborative efforts. Of particular interest is the exploration of the transferability of techniques across different cryptographic applications and the strengthening of AI security mechanisms. Furthermore, we will delve into recent developments, including those stemming from previous AICrypt events, to provide insights into the evolving landscape of this field.

Download the Call for Papers

Topics of Interest

Authors interested to give a contributed talk in this workshop are invited to submit an extended abstract of at most 2 pages (excluding references) using Easychair.

The topics of the workshop encompass all aspects concerning the intersection of AI and cryptography, including but not limited to:

- Deep learning-based cryptanalysis (e.g., neural distinguishers)
- Explainability and interpretability of AI models for cryptanalysis
- Deep learning techniques for Side-Channel Analysis
- AI-assisted design of cryptographic primitives and protocols
- AI-driven attacks on cryptographic protocols
- Cryptographic countermeasures for security and privacy of AI systems

Submission

We encourage researchers working on all aspects of AI and cryptography to take the opportunity and use AICrypt to share their work and participate in discussions. The authors are invited to submit an extended abstract using the EasyChair submission system.

Submitted abstracts for contributed talks will be reviewed by the workshop organizers for suitability and interest to the AICrypt audience. There are no formal proceedings published in this workshop, thus authors can submit extended abstracts related to works submitted or recently published in other venues, or work in progress that they plan to submit elsewhere.

The authors of accepted papers will be invited to submit an extended version of paper to appear (after a new round of reviewing) in a post-proceedings volume to be published by Springer.

Every accepted submission must have at least one author registered for the workshop. All submitted abstracts must follow the original LNCS format with a page limit of up to 2 pages (excluding references). The abstracts should be submitted electronically in PDF format.

Important dates (AoE)

EXTENDED submission deadline!

Abstract submission deadline: APR 15, 2024

previously APR 5, 2024

Notification to authors: APR 19, 2024

Workshop date: May 26, 2024

Registration

Workshop registration goes through the Eurocrypt registration process. Check this page for further information.

Keynotes

AI: The Good, the Bad and the Ugly

Bart Preneel

COSIC, KU Leuven, Belgium

The breakthroughs in AI have led to the belief that AI will revolutionize society and will result to a different approach towards cybersecurity. However, researchers caution that beyond the hype, there are significant privacy risks, potential abuse by malicious actors, and the possibility of incorrect or unfair decisions made by AI systems. Privacy preserving machine learning uses techniques such as computing on encrypted data to mitigate the privacy risks. Additionally, legal frameworks such as the EU AI Act and the Council of Europe's AI treaty are being developed to address some other issues. This talk presents a perspective on these developments.

Prof. Bart Preneel, a full professor at KU Leuven, leads the renowned COSIC research group. His expertise lies in applied cryptography, cybersecurity, and privacy. Prof. Preneel has delivered over 150 invited talks across 50 countries and received the RSA Award for Excellence in Mathematics (2014) and the ESORICS Outstanding Research Award (2017). He served as president of IACR (International Association for Cryptologic Research) and is also a fellow of the IACR. Prof. Preneel consults for industry and government on cybersecurity and privacy, he founded the mobile authentication startup nextAuth and holds roles in Approach Belgium, Tioga Capital Partners, and Nym Technologies. Actively engaged in cybersecurity policy, he contributes to ENISA as an Advisory Group member.

Facial Misrecognition Systems

Adi Shamir

Weizmann Institute of Science, Rehovot, Israel

In this talk I will describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (i.e., without using any additional training or optimization). These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other's existence with almost no interference.
Joint work with Irad Zehavi and Roee Nitzan.

Adi Shamir is an Israeli cryptographer and inventor. He is a co-inventor of the Rivest–Shamir–Adleman (RSA) algorithm (along with Ron Rivest and Len Adleman), a co-inventor of the Feige–Fiat–Shamir identification scheme (along with Uriel Feige and Amos Fiat), one of the inventors of differential cryptanalysis and has made numerous contributions to the fields of cryptography and computer science. In 2002, he won the Turing Award, together with Rivest and Adleman, in recognition of his contributions to cryptography. He now works at the faculty of Mathematics and Computer Science at the Weizmann Institute of Science.

Touching Points of Cryptography and AI

Moti Yung

Google & Columbia University, New York, USA

AI is based on statistical approximations and attempts to get some big picture from sampling. Crypto is based on careful models, exact assumptions, and proofs. So these two areas seem to be away from each other. In this talk, I will show that there are enough common issues that need attention and further research.

Moti Yung is a computer scientist, cryptographer and information security researcher. Yung’s areas of expertise include cryptovirology and kleptography. He is a a Security and Privacy Research Scientist with Google and an Adjunct Research Faculty at the Computer Science Department at Columbia University, where he earned his Ph.D.
Yung's contributions to research and development treat science and technology holistically: from the theoretical mathematical foundations, via conceptual mechanisms which typify computer science, to participation in the design and development of industrial products. His industry experience includes IBM Research, Certco/ Bankers Trust, RSA Laboratories (EMC), and Snap.
His published work (articles, patents, a book, and edited books) includes collaborations with more than 300 co-authors. Yung’s work has been predicting future needs of secure systems, and analyzing coming threats. These led to basic theoretical and applied notions, like: ransomware attacks, cryptosystems subversion, concurrent sessions in authentication protocols, strong (chosen ciphertext) secure encryption, and digital signatures from simplified cryptography.

Accepted Abstracts

Provable Learnability Assessment of PUFs in Pre-silicon Phase

Durba Chatterjee

PrivaTree: Private Decision Tree Evaluation by means of Homomorphic Encryption

Marina Checri, Aymen Boudguiga, Jean-Paul Bultel, Olive Chakraborty, Pierre-Emmanuel Clet and Renaud Sirdey

5 Years of Neural Distinguishers

David Gerault and Anna Hambitzer

Efficient Verification Framework for Large-Scale Machine Learning Models

Artem Grigor, Anton Kravchenko and Georg Wiese

The more, the merrier? A step-by-step inter-device analysis for transfer learning side-channel attacks

Lizzy Grootjen, Zhuoran Liu and Ileana Buhan

Non-Interactive Secure Aggregation and its Applications to Federated Learning

Harish Karthikeyan and Antigoni Polychroniadou

Exploring DNN Weights Extraction via Deep Learning Physical Side-Channel Analysis

Dirk Lauret and Zhuoran Liu

Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data

Leonardo Neumann, Antonio Guimarães, Diego F. Aranha and Edson Borin

Ensuring Privacy and Robustness in Computation of Machine Learning Algorithms

Chrysa Oikonomou and Katerina Sotiraki

Encrypted Image Classification with Low Memory Footprint using Fully Homomorphic Encryption

Lorenzo Rovida and Alberto Leporati

Program

The program starts at 09:00 am, CEST time (UTC + 2).

TIME CEST (UTC+2)	SESSION/TITLE
Session 1: Side-Channel Analysis 09:00 - 10:40
09:00 - 10:00	Opening and Keynote Talk: AI: The Good, the Bad and the Ugly Bart Preneel
10:00 - 10:20	The more, the merrier? A step-by-step inter-device analysis for transfer learning side-channel attacks Lizzy Grootjen, Zhuoran Liu and Ileana Buhan
10:20 - 10:40	Exploring DNN Weights Extraction via Deep Learning Physical Side-Channel Analysis Dirk Lauret and Zhuoran Liu
10:40 - 11:00	Coffee Break
Session 2: Homomorphic Encryption and Verification of ML 11:00 - 12:40
11:00 - 11:20	Encrypted Image Classification with Low Memory Footprint using Fully Homomorphic Encryption Lorenzo Rovida and Alberto Leporati
11:20 - 11:40	Homomorphic WiSARDs: Efficient Weightless Neural Network training over encrypted data Leonardo Neumann, Antonio Guimarães, Diego F. Aranha and Edson Borin
11:40 - 12:00	PrivaTree: Private Decision Tree Evaluation by means of Homomorphic Encryption Marina Checri, Aymen Boudguiga, Jean-Paul Bultel, Olive Chakraborty, Pierre-Emmanuel Clet and Renaud Sirdey
12:00 - 12:20	Efficient Verification Framework for Large-Scale Machine Learning Models Artem Grigor, Anton Kravchenko and Georg Wiese
12:20 - 12:40	Ensuring Privacy and Robustness in Computation of Machine Learning Algorithms Chrysa Oikonomou and Katerina Sotiraki
12:40 - 13:30	Lunch Break
Session 3: Federated Learning 13:30 - 14:50
13:30 - 14:30	Keynote Talk: Facial Misrecognition Systems Adi Shamir
14:30 - 14:50	Non-Interactive Secure Aggregation and its Applications to Federated Learning Harish Karthikeyan and Antigoni Polychroniadou
14:50 - 15:20	Coffee break
Session 4: Neural distinguishers & PUFs 15:20 - 17:00
15:20 - 16:20	Keynote Talk: Touching Points of Cryptography and AI Moti Yung
16:20 - 16:40	5 Years of Neural Distinguishers David Gerault and Anna Hambitzer
16:40 - 17:00	Provable Learnability Assessment of PUFs in Pre-silicon Phase Durba Chatterjee