差分隐私模型的启发式隐私参数设置策略

Heuristic privacy parameter setting strategy for differential privacy model

隐私参数ε能度量隐私保护程度及噪声量,但是其设置只能依赖于实验或专业人士经验,限制了差分隐私模型的使用与推广。针对这个问题,基于(ρ_1,ρ_2)-隐私模型提出一种启发式的隐私参数ε设置策略(limit privacy breaches in differential privacy,LPBDP),分析隐私参数ε与(ρ_1,ρ_2)的内在联系,实现噪声量的添加由(ρ_1,ρ_2)决定。LPBDP通过如下启发式原则设置隐私参数ε:如果攻击者关于目标受害者的先验概率小于阈值ρ_1,攻击者得到差分隐私查询策略返回的加噪结果后,关于目标受害者的后验概率必须小于阈值ρ_2。实验表明,LPBDP能够更直观地设置隐私参数ε以满足差分隐私约束。

The privacy parameter ε can measure the degree of privacy protection and the amount of noise,however,the setting of the privacy parameter ε can only depend on the experience of the lab or the professional experience,limiting the adoption and popularize of the differential privacy model. Aiming at this problem,this paper proposed a kind of heuristic privacy parameter ε setting strategy( limit privacy breaches in differential privacy,LPBDP) based on the( ρ1,ρ2)-privacy model. It analyzed the intrinsic relationship between the privacy parameter ε and( ρ1,ρ2),and determined the addition of the noise quantity by the parameters( ρ1,ρ2). LPBDP set the privacy parameter ε by the following heuristic principle: if the attacker’s prior probability of the target victim was less than the threshold ρ1,then,the attacker’s posterior probability of the victim of the target must be less than threshold ρ2. Experiments show that LPBDP can more visually set the privacy parameter ε to meet the differential privacy constraints.