Side-channel attacks allow extracting secret information from the execution
of cryptographic primitives by correlating the partially known computed data
and the measured side-channel signal. However, to set up a successful
side-channel attack, the attacker has to perform i) the challenging task of
locating the time instant in which the target cryptographic primitive is
executed inside a side-channel trace and then ii)the time-alignment of the
measured data on that time instant. This paper presents a novel deep-learning
technique to locate the time instant in which the target computed cryptographic
operations are executed in the side-channel trace. In contrast to
state-of-the-art solutions, the proposed methodology works even in the presence
of trace deformations obtained through random delay insertion techniques. We
validated our proposal through a successful attack against a variety of
unprotected and protected cryptographic primitives that have been executed on
an FPGA-implemented system-on-chip featuring a RISC-V CPU.

通过深度学习技术，本文提出了一种新颖的方法来定位侧信道迹线中执行目标计算密集型密码操作的时间点。与现有解决方案的区别在于，该方法能够在通过随机延迟插入技术获得的迹线变形的情况下正常工作。在实现了 RISC-V CPU 的 FPGA 系统上，通过成功攻击各种未受保护和受保护的密码原语进行了验证。

在侧信道跟踪中定位加密操作的深度学习技术

A Deep-Learning Technique to Locate Cryptographic Operations in  Side-Channel Traces

Federated learning enables users to collaboratively train a machine learning
model over their private datasets. Secure aggregation protocols are employed to
mitigate information leakage about the local datasets. This setup, however,
still leaks the participation of a user in a training iteration, which can also
be sensitive. Protecting user anonymity is even more challenging in dynamic
environments where users may (re)join or leave the training process at any
point of time. In this paper, we introduce AnoFel, the first framework to
support private and anonymous dynamic participation in federated learning.
AnoFel leverages several cryptographic primitives, the concept of anonymity
sets, differential privacy, and a public bulletin board to support anonymous
user registration, as well as unlinkable and confidential model updates
submission. Additionally, our system allows dynamic participation, where users
can join or leave at any time, without needing any recovery protocol or
interaction. To assess security, we formalize a notion for privacy and
anonymity in federated learning, and formally prove that AnoFel satisfies this
notion. To the best of our knowledge, our system is the first solution with
provable anonymity guarantees. To assess efficiency, we provide a concrete
implementation of AnoFel, and conduct experiments showing its ability to
support learning applications scaling to a large number of clients. For an
MNIST classification task with 512 clients, the client setup takes less than 3
sec, and a training iteration can be finished in 3.2 sec. We also compare our
system with prior work and demonstrate its practicality for contemporary
learning tasks.

AnoFel 是第一个支持联邦学习中动态参与的私密和匿名框架，它利用密码原语，匿名集合的概念，差分隐私和公共公告板来支持匿名用户注册，以及不可链接和机密模型更新提交。