Spiking Neural P systems are a class of membrane computing models inspired
directly by biological neurons. Besides the theoretical progress made in this
new computational model, there are also numerous applications of P systems in
fields like formal verification, artificial intelligence, or cryptography.
Motivated by all the use cases of SN P systems, in this paper, we present a new
privacy-preserving protocol that enables a client to compute a linear function
using an SN P system hosted on a remote server. Our protocol allows the client
to use the server to evaluate functions of the form t_1k + t_2 without
revealing t_1, t_2 or k and without the server knowing the result. We also
present an SN P system to implement any linear function over natural numbers
and some security considerations of our protocol in the honest-but-curious
security model.

通过使用 Spiking Neural P 系统作为计算模型，本文提出了一种新的隐私保护协议，允许客户端在远程服务器上计算一个线性函数，并且不泄露任何输入和结果信息，同时保证服务器的安全性。

脉冲神经 P 系统中的隐私保护线性计算

Privacy-preserving Linear Computations in Spiking Neural P Systems

Fair machine learning is a thriving and vibrant research topic. In this
paper, we propose Fairness as a Service (FaaS), a secure, verifiable and
privacy-preserving protocol to computes and verify the fairness of any machine
learning (ML) model. In the deisgn of FaaS, the data and outcomes are
represented through cryptograms to ensure privacy. Also, zero knowledge proofs
guarantee the well-formedness of the cryptograms and underlying data. FaaS is
model--agnostic and can support various fairness metrics; hence, it can be used
as a service to audit the fairness of any ML model. Our solution requires no
trusted third party or private channels for the computation of the fairness
metric. The security guarantees and commitments are implemented in a way that
every step is securely transparent and verifiable from the start to the end of
the process. The cryptograms of all input data are publicly available for
everyone, e.g., auditors, social activists and experts, to verify the
correctness of the process. We implemented FaaS to investigate performance and
demonstrate the successful use of FaaS for a publicly available data set with
thousands of entries.

通过使用一种安全、可验证且保护隐私的协议，Fairness as a Service (FaaS) 提供了一种计算和验证任何机器学习模型公平性的方法，并通过加密保护数据和结果的隐私，同时零知识证明和多种公平性指标的支持使其能够作为一种服务来审计任何机器学习模型的公平性。

可验证公正性：机器学习系统中隐私保护的公平计算

Verifiable Fairness: Privacy-preserving Computation of Fairness for  Machine Learning Systems

Vertical federated learning (VFL) is a promising category of federated
learning for the scenario where data is vertically partitioned and distributed
among parties. VFL enriches the description of samples using features from
different parties to improve model capacity. Compared with horizontal federated
learning, in most cases, VFL is applied in the commercial cooperation scenario
of companies. Therefore, VFL contains tremendous business values. In the past
few years, VFL has attracted more and more attention in both academia and
industry. In this paper, we systematically investigate the current work of VFL
from a layered perspective. From the hardware layer to the vertical federated
system layer, researchers contribute to various aspects of VFL. Moreover, the
application of VFL has covered a wide range of areas, e.g., finance,
healthcare, etc. At each layer, we categorize the existing work and explore the
challenges for the convenience of further research and development of VFL.
Especially, we design a novel MOSP tree taxonomy to analyze the core component
of VFL, i.e., secure vertical federated machine learning algorithm. Our
taxonomy considers four dimensions, i.e., machine learning model (M),
protection object (O), security model (S), and privacy-preserving protocol (P),
and provides a comprehensive investigation.

本文从硬件层到垂直联邦系统层对垂直联邦学习的现有工作和挑战进行分类，特别设计了一种新的 MOSP 树分类法来分析保护对象、安全模型和隐私保护协议等四个维度的核心组件 - 安全垂直联邦机器学习算法。

基于分层视角的垂直联邦学习综述

A Survey on Vertical Federated Learning: From a Layered Perspective

Traditional electricity meters are replaced by Smart Meters in customers'
households. Smart Meters collects fine-grained utility consumption profiles
from customers, which in turn enables the introduction of dynamic, time-of-use
tariffs. However, the fine-grained usage data that is compiled in this process
also allows to infer the inhabitant's personal schedules and habits. We propose
a privacy-preserving protocol that enables billing with time-of-use tariffs
without disclosing the actual consumption profile to the supplier. Our approach
relies on a zero-knowledge proof based on Pedersen Commitments performed by a
plug-in privacy component that is put into the communication link between Smart
Meter and supplier's back-end system. We require no changes to the Smart Meter
hardware and only small changes to the software of Smart Meter and back-end
system. In this paper we describe the functional and privacy requirements, the
specification and security proof of our solution and give a performance
evaluation of a prototypical implementation.

在不泄露居民用电数据的情况下，借助基于 Pedersen 承诺的零知识证明及插件的隐私保护协议，使用智能电表收集电表数据，并通过实现动态时间使用费率实现计费。