论文标题
基于分布式属性的私人访问控制
Distributed Attribute-based Private Access Control
论文作者
论文摘要
在基于属性的访问控件中，具有某些验证属性的用户将访问某些特定数据。关于用户属性的隐私，我们研究了带有多个权威的基于分布式属性的私人访问控制（DAPAC）的问题，每个机构将仅学习和验证其中一个属性。 为了调查其基本限制，我们引入了一个信息理论DAPAC框架，并使用$ n \ in \ Mathbb {n} $，$ n \ geq 2 $，复制的非碰撞服务器（当局）和一些用户。每个用户都有一个属性vector $ \ mathbf {v^*} =（v_1^*，...，v_n^*）dimension $ n $的$，并且有资格检索消息$ w^{\ mathbf {v}^*} $，在所有服务器中可用。 [n] $中的每个服务器$ n \都只能观察和验证用户的$ n $'属性。作为响应，它将数据的函数发送给用户。系统必须满足以下条件：（1）正确性：具有属性向量$ \ MathBf {v^*} $的用户能够检索他的预期消息$ W^{\ Mathbf {v}^*} $从服务器的响应中响应中的响应，（2）数据，（2）数据不超过其他信息。用户。 DAPAC的容量定义为文件大小的比率和响应的汇总大小，在所有可行方案上最大化。我们通过提出具有速率$ \ frac {1} {2k} $的可实现算法来获得该问题的下限，其中$ k $是每个属性的字母的大小。
In attribute-based access control, users with certain verified attributes will gain access to some particular data. Concerning with privacy of the users' attributes, we study the problem of distributed attribute-based private access control (DAPAC) with multiple authorities, where each authority will learn and verify only one of the attributes. To investigate its fundamental limits, we introduce an information theoretic DAPAC framework, with $N \in \mathbb{N}$, $N\geq 2$, replicated non-colluding servers (authorities) and some users. Each user has an attribute vector $\mathbf{v^*}=(v_1^*, ..., v_N^*)$ of dimension $N$ and is eligible to retrieve a message $W^{\mathbf{v}^*}$, available in all servers. Each server $n\in [N]$ is able to only observe and verify the $n$'th attribute of a user. In response, it sends a function of its data to the user. The system must satisfy the following conditions: (1) Correctness: the user with attribute vector $\mathbf{v^*}$ is able to retrieve his intended message $W^{\mathbf{v}^*}$ from the servers' response, (2) Data Secrecy: the user will not learn anything about the other messages, (3) Attribute Privacy: each Server~$n$ learns nothing beyond attribute $n$ of the user. The capacity of the DAPAC is defined as the ratio of the file size and the aggregated size of the responses, maximized over all feasible schemes. We obtain a lower bound on the capacity of this problem by proposing an achievable algorithm with rate $\frac{1}{2K}$, where $K$ is the size of the alphabet of each attribute.
