Android Permission机制的实现与安全分析

Permission机制作为Android安全的重要组成部分,受到了越来越多的关注,已有的研究主要集中在对应用程序申请的Permission进行静态分析上。文章从分析Permission机制的实现过程入手,分析了Permission机制自身的安全问题,发现了一个Permission机制漏洞。应用程序利用该漏洞可以绕过权限管理,主动提升应用程序的访问权限,为后续攻击提供支持。同时文章也对Permission机制其它方面的安全特性进行了分析。     

A general transformation from KP-ABE to searchable encryption

Users are inclined to share sensitive data in a remote server if no strong security mechanism is in place. Searchable encryption satisfies the need of users to execute a search encrypted data. Previous searchable encryption methods such as “public key encryption with keyword search (PEKS)” restricted the data access to certain users, because only the assigned users were able to search the encrypted data. In this paper we will discuss the relation between Attribute Based Encryption (ABE) and searchable encryption and define a weak anonymity of the ABE scheme, named “attribute privacy”. With this weak anonymity, we propose a general transformation from ABE to Attribute Based Encryption with Keyword Search (ABEKS) and a concrete attribute private key-policy ABE (KP-ABE) scheme. We present an ABEKS scheme based on this KP-ABE scheme and permit multi-users to execute a flexible search on the remote encrypted data.     

Schemes for remotely preparing an arbitrary four-qubit \chi -state

Two schemes via different entangled resources as the quantum channel are proposed to realize remote preparation of an arbitrary four-particle \(\chi \) -state with high success probabilities. To design these protocols, some useful and general measurement bases are constructed, which have no restrictions on the coefficients of the prepared states. It is shown that through a four-particle projective measurement and two-step three-particle projective measurement under the novel sets of mutually orthogonal basis vectors, the original state can be prepared with the probability 50 and 100 %, respectively. And for the first scheme, the special cases of the prepared state that the success probability reaches up to 100 % are discussed by the permutation group. Furthermore, the present schemes are extended to the non-maximally entangled quantum channel, and the classical communication costs are calculated.     

基于有限状态机的RFID流数据过滤与清理技术

为了实现从大量不可靠、冗余的RFID（radio frequency identification）流数据中提取有效信息,提高RFID系统中数据的质量,提出了一种基于有限状态机的RFID 流数据过滤与清理方法.实验结果表明：该方法能够有效过滤系统外标签数据,清理系统内部冗余标签数据,筛选有效标签数据,并能够降低漏读、误读带来的风险.最后,利用地理信息系统的可视化技术,将过滤与清理结果展示在地图上.     

Temporal evolution of contacts and communities in networks of face-to-face human interactions

Temporal dynamics of social interaction networks as well as the analysis of communities are key aspects to gain a better understanding of the involved processes, important influence factors, their effects, and their structural implications. In this article, we analyze temporal dynamics of contacts and the evolution of communities in networks of face-to-face proximity. As our application context, we consider four scientific coil- ferences. On a structural level, we focus on static and dynamic properties of the contact graphs. Also, we analyze the resulting community structure using state-of-the-art automatic community detection algorithms. Specifically, we analyze tile evolution of contacts and communities over time to consider the stability of the respective conmmnities. Furthermore, we assess different factors which have an influence on the quality of com- munity prediction. Overall, we provide first important insights into the evolution of contacts and communities in face-to-face contact networks.     

一种无监督的窃密攻击及时发现方法

近年来,窃密攻击成为了最严重的网络安全威胁之一.除了恶意软件,人也可以成为窃密攻击的实施主体,尤其是组织或企业的内部人员.由人实施的窃密很少留下明显的异常痕迹,给真实场景中攻击的及时发现和窃密操作的分析还原带来了挑战.提出了一个方法,将每个用户视为独立的主体,通过对比用户当前行为事件与其历史正常行为的偏差检测异常,以会话为单元的检测实现了攻击发现的及时性,采用无监督算法避免了对大量带标签数据的依赖,更能适用于真实场景.对算法检测为异常的会话,进一步提出事件链构建方法,一方面还原具体窃密操作,另一方面通过与窃密攻击模式对比,更精确地判断攻击.在卡内基梅隆大学的CERT内部威胁数据集上进行了实验,结果达到99%以上的准确率,且可以做到无漏报、低误报,证明了方法的有效性和优越性.     

面向用户的支持用户掉线的联邦学习数据隐私保护方法

联邦学习是解决多组织协同训练问题的一种有效手段,但是现有的联邦学习存在不支持用户掉线、模型API泄露敏感信息等问题。文章提出一种面向用户的支持用户掉线的联邦学习数据隐私保护方法,可以在用户掉线和保护的模型参数下训练出一个差分隐私扰动模型。该方法利用联邦学习框架设计了基于深度学习的数据隐私保护模型,主要包含两个执行协议:服务器和用户执行协议。用户在本地训练一个深度模型,在本地模型参数上添加差分隐私扰动,在聚合的参数上添加掉线用户的噪声和,使得联邦学习过程满足(ε,δ)-差分隐私。实验表明,当用户数为50、ε=1时,可以在模型隐私性与可用性之间达到平衡。     

进程安全保护技术综述

进程安全保护是信息安全领域的一个研究热点,可以应用于计算机安全系统、网站保护、杀毒软件自保护、数字产权保护等方面.在信息安全研究的背景下,总结了进程所受到的威胁,包括软件和硬件方面的威胁;结合实际案例对进程安全保护技术从硬件和软件方面进行了详细分类,分析了每种技术的特点和不足;指出现在研究存在的问题,并展望了未来关于进程安全保护的研究方向和重点.     

物联网移动节点直接匿名漫游认证协议

无线网络下传统匿名漫游协议中远程域认证服务器无法直接完成对移动节点的身份合法性验证,必须在家乡域认证服务器的协助下才能完成,导致漫游通信时延较大,无法满足物联网感知子网的快速漫游需求.针对上述不足,提出可证安全的物联网移动节点直接匿名漫游认证协议,远程域认证服务器通过与移动节点间的1轮消息交互,可直接完成对移动节点的身份合法性验证.该协议在实现移动节点身份合法性验证的同时,具有更小的通信时延、良好的抗攻击能力和较高的执行效率.相较于传统匿名漫游协议而言,该协议快速漫游的特点更适用于物联网环境.安全性证明表明,该协议在CK安全模型下是可证安全的.