论文标题
关于信息的渐近能力理论保护隐私的流行病学数据收集
On the Asymptotic Capacity of Information Theoretical Privacy-preserving Epidemiological Data Collection
论文作者
论文摘要
我们制定了一个新的安全分布式计算问题，其中仿真中心可以通过由$ n $服务器组成的缓存层进行$ k $用户数据的任何线性组合。用户，服务器和数据收集器不相互信任。对于用户，任何数据都必须受到最多$ e $服务器的保护；对于服务器，与所需线性组合相比，更多的信息不能泄漏到数据收集器；对于数据收集器，任何单个服务器对线性组合的系数一无所知。我们的目标是找到最佳的下载成本，该成本定义为由服务器上传到仿真中心的消息大小，达到所需的线性组合的大小。当$ e <n-1 $时，我们提出了一个具有最佳下载成本的计划。我们还证明，当$ e \ geq n-1 $时，该方案是不可行的。
We formulate a new secure distributed computation problem, where a simulation center can require any linear combination of $ K $ users' data through a caching layer consisting of $ N $ servers. The users, servers, and data collector do not trust each other. For users, any data is required to be protected from up to $ E $ servers; for servers, any more information than the desired linear combination cannot be leaked to the data collector; and for the data collector, any single server knows nothing about the coefficients of the linear combination. Our goal is to find the optimal download cost, which is defined as the size of message uploaded to the simulation center by the servers, to the size of desired linear combination. We proposed a scheme with the optimal download cost when $E < N-1$. We also prove that when $E\geq N-1$, the scheme is not feasible.
