Differential privacy is a notion that has emerged in the community of statistical databases, as a response to the problem of protecting the privacy of the database's participants when performing statistical queries. The idea is that a randomized query satisfies differential privacy if the likelihood of obtaining a certain answer for a database x is not too different from the likelihood of obtaining the same answer on adjacent databases, i.e. databases which differ from x for only one individual. Information flow is an area of Security concerned with the problem of controlling the leakage of confidential information in programs and protocols. Nowadays, one of the most established approaches to quantify and to reason about leakage is based on the Renyi min entropy version of information theory. In this paper, we analyze critically the notion of differential privacy in light of the conceptual framework provided by the Renyi min information theory. We show that there is a close relation between differential privacy and leakage, due to the graph symmetries induced by the adjacency relation. Furthermore, we consider the utility of the randomized answer, which measures its expected degree of accuracy. We focus on certain kinds of utility functions called "binary", which have a close correspondence with the Renyi min mutual information. Again, it turns out that there can be a tight correspondence between differential privacy and utility, depending on the symmetries induced by the adjacency relation and by the query. Depending on these symmetries we can also build an optimal-utility randomization mechanism while preserving the required level of differential privacy. Our main contribution is a study of the kind of structures that can be induced by the adjacency relation and the query, and how to use them to derive bounds on the leakage and achieve the optimal utility.
As far as we know, the first work to investigate the relation between differential privacy and information-theoretic leakage was @cite_16 . In this work, a channel is relative to a given database @math , and the channel inputs are all possible databases adjacent to @math . Two bounds on leakage were presented, one for teh R 'enyi min entropy, and one for Shannon entropy. Our bound in Proposition is an improvement with respect to the (R 'enyi min entropy) bound in @cite_16 .
@cite_16
2167136065 43706173 2243636408 2229752136
Recent research in quantitative theories for information-hiding topics, such as Anonymity and Secure Information Flow, tend to converge towards the idea of modeling the system as a noisy channel in the information-theoretic sense. The notion of information leakage, or vulnerability of the system, has been related in some approaches to the concept of mutual information of the channel. A recent work of Smith has shown, however, that if the attack consists in one single try, then the mutual information and other concepts based on Shannon entropy are not suitable, and he has proposed to use Renyi's min-entropy instead. In this paper, we consider and compare two different possibilities of defining the leakage, based on the Bayes risk, a concept related to Renyi min-entropy. Differential privacy is a notion of privacy that has become very popular in the database community. Roughly, the idea is that a randomized query mechanism provides sufficient privacy protection if the ratio between the probabilities of two different entries to originate a certain answer is bound by e^e. In the fields of anonymity and information flow there is a similar concern for controlling information leakage, i.e. limiting the possibility of inferring the secret information from the observables. In recent years, researchers have proposed to quantify the leakage in terms of the information-theoretic notion of mutual information. There are two main approaches that fall in this category: One based on Shannon entropy, and one based on Renyi's min entropy. The latter has connection with the so-called Bayes risk, which expresses the probability of guessing the secret. In this paper, we show how to model the query system in terms of an information-theoretic channel, and we compare the notion of differential privacy with that of mutual information. We show that the notion of differential privacy is strictly stronger, in the sense that it implies a bound on the mutual information, but not viceversa. This note describes an information theory problem that arose from some analysis of quantum key distribution protocols. The problem seems very natural and is very easy to state but has not to our knowledge been addressed before in the information theory literature: suppose that we have a random bit string y of length n and we reveal k bits at random positions, preserving the order but without revealing the positions, how much information about y is revealed? We show that while the cardinality of the set of compatible y strings depends only on n and k, the amount of leakage does depend on the exact revealed x string. We observe that the maximal leakage, measured as decrease in the Shannon entropy of the space of possible bit strings, corresponds to the x string being all zeros or all ones and that the minimum leakage corresponds to the alternating x strings. We derive a formula for the maximum leakage minimal entropy in terms of n and k. We discuss the relevance of other measures of information, in particular min-entropy, in a cryptographic context. Finally, we describe a simulation tool to explore these results. The data processing inequality states that the quantum relative entropy between two states ρ and σ can never increase by applying the same quantum channel N to both states. This inequality can be strengthened with a remainder term in the form of a distance between ρ and the closest recovered state (R∘N)(ρ), where R is a recovery map with the property that σ=(R∘N)(σ). We show the existence of an explicit recovery map that is universal in the sense that it depends only on σ and the quantum channel N to be reversed. This result gives an alternate, information-theoretic characterization of the conditions for approximate quantum error correction.