Recent works in cyber deception study how to deter malicious intrusion by
generating multiple fake versions of a critical document to impose costs on
adversaries who need to identify the correct information. However, existing
approaches are context-agnostic, resulting in sub-optimal and unvaried outputs.
We propose a novel context-aware model, Fake Document Infilling (FDI), by
converting the problem to a controllable mask-then-infill procedure. FDI masks
important concepts of varied lengths in the document, then infills a realistic
but fake alternative considering both the previous and future contexts. We
conduct comprehensive evaluations on technical documents and news stories.
Results show that FDI outperforms the baselines in generating highly believable
fakes with moderate modification to protect critical information and deceive
adversaries.

研究人员提出了一种基于情境感知的虚假文件填充 (FDI) 模型，将问题转化为可控的遮蔽和填充过程，可以更好地保护关键信息和欺骗对手。研究发现，FDI 在技术文档和新闻报道中生成了高度可信的虚假信息。

可控的虚假文件填充用于网络欺骗

Controllable Fake Document Infilling for Cyber Deception

In this work we present three different randomized gossip algorithms for
solving the average consensus problem while at the same time protecting the
information about the initial private values stored at the nodes. We give
iteration complexity bounds for all methods, and perform extensive numerical
experiments.

本文提出三种不同的随机流言算法，旨在解决平均共识问题的同时保护存储节点初始私有值的信息，并对所有方法给出了迭代复杂度的界限，进行了广泛的数值实验。

隐私保护随机八卦算法

Privacy Preserving Randomized Gossip Algorithms

Neural network has been attracting more and more researchers since the past
decades. The properties, such as parameter sensitivity, random similarity,
learning ability, etc., make it suitable for information protection, such as
data encryption, data authentication, intrusion detection, etc. In this paper,
by investigating neural networks' properties, the low-cost authentication
method based on neural networks is proposed and used to authenticate images or
videos. The authentication method can detect whether the images or videos are
modified maliciously. Firstly, this chapter introduces neural networks'
properties, such as parameter sensitivity, random similarity, diffusion
property, confusion property, one-way property, etc. Secondly, the chapter
gives an introduction to neural network based protection methods. Thirdly, an
image or video authentication scheme based on neural networks is presented, and
its performances, including security, robustness and efficiency, are analyzed.
Finally, conclusions are drawn, and some open issues in this field are
presented.

通过研究神经网络的性质，提出了基于神经网络的低成本身份验证方法，用于身份验证图像或视频，以检测它们是否被恶意修改