Differentially Private Data Publishing and Analysis: A Survey <s> Relaxation of Differential Privacy <s> We introduce Concentrated Differential Privacy, a relaxation of Differential Privacy enjoying better accuracy than both pure differential privacy and its popular "(epsilon,delta)" relaxation without compromising on cumulative privacy loss over multiple computations. <s> BIB001 </s> Differentially Private Data Publishing and Analysis: A Survey <s> Relaxation of Differential Privacy <s> "Concentrated differential privacy" was recently introduced by Dwork and Rothblum as a relaxation of differential privacy, which permits sharper analyses of many privacy-preserving computations. We present an alternative formulation of the concept of concentrated differential privacy in terms of the Renyi divergence between the distributions obtained by running an algorithm on neighboring inputs. With this reformulation in hand, we prove sharper quantitative results, establish lower bounds, and raise a few new questions. We also unify this approach with approximate differential privacy by giving an appropriate definition of "approximate concentrated differential privacy." <s> BIB002
Differential privacy is indeed an excellent definition to guarantee strict privacy levels. The most attractive feature is that the privacy level degrades smoothly under composition theory. As pure differential privacy is too strict in practical terms, a relaxation of differential privacy is highly desired. Approximate or (; d)-differential privacy is widely used as a method of relaxation and has been discussed heavily in this paper. However, the bound analysis for the composition of approximate differential privacy is hard  , even for simple computations, making it inconvenient for use in real-world applications. Dwork et al. BIB001 developed a new relaxation technique called concentrated differential privacy, in which the privacy loss in multiple computations has a small mean and is subGaussian. Based on this new technique, Bun BIB002 proposed zero-concentrated differential privacy to minimize the mean of privacy losses to zero, to improve the bound of d. These relaxation definitions need to be further explored. For example, designing randomized algorithms to achieve the new definitions and analyzing the sample complexity of those algorithms still remain problems. Generally, previous work on differential privacy can be re-visited through the perspective of new definitions, which may lead to new insights and results.