arXiv subjects

Rüdiger Reischuk

Publications and source records attributed to Rüdiger Reischuk.

4 recordsLinked to original sources

arXiv2026

Parallel Composition for Statistical Privacy

Differential Privacy (DP) considers a scenario in which an adversary has almost complete information about the entries of a database. This worst-case assumption is likely to overestimate the privacy threat faced by an individual in practice. In contrast, Statistical Privacy (SP), as well as related notions such as noiseless privacy or limited background knowledge privacy, describe a setting in which the adversary knows the distribution of the database entries, but not their exact realizations. In this case, privacy analysis must account for the interaction between uncertainty induced by the entropy of the underlying distributions and privacy mechanisms that distort query answers, which can be highly non-trivial. This paper investigates this problem for multiple queries (composition). A privacy mechanism is proposed that is based on subsampling and randomly partitioning the database to bound the dependency among queries. This way for the first time, to the best of our knowledge, upper privacy bounds against limited adversaries are obtained without any further restriction on the database. These bounds show that in realistic application scenarios taking the entropy of distributions into account yields improvements of privacy and precision guarantees. We illustrate examples where for fixed privacy parameters and utility loss SP allows significantly more queries than DP.

cs.CR↗

arXiv2025

Improving Statistical Privacy by Subsampling

Differential privacy (DP) considers a scenario, where an adversary has almost complete information about the entries of a database This worst-case assumption is likely to overestimate the privacy thread for an individual in real life. Statistical privacy (SP) denotes a setting where only the distribution of the database entries is known to an adversary, but not their exact values. In this case one has to analyze the interaction between noiseless privacy based on the entropy of distributions and privacy mechanisms that distort the answers of queries, which can be quite complex. A privacy mechanism often used is to take samples of the data for answering a query. This paper proves precise bounds how much different methods of sampling increase privacy in the statistical setting with respect to database size and sampling rate. They allow us to deduce when and how much sampling provides an improvement and how far this depends on the privacy parameter ε. To perform these investigations we develop a framework to model sampling techniques. For the DP setting tradeoff functions have been proposed as a finer measure for privacy compared to (ε,δ)-pairs. We apply these tools to statistical privacy with subsampling to get a comparable characterization

cs.CR↗

arXiv2025

Statistical Privacy

To analyze the privacy guarantee of personal data in a database that is subject to queries it is necessary to model the prior knowledge of a possible attacker. Differential privacy considers a worst-case scenario where he knows almost everything, which in many applications is unrealistic and requires a large utility loss. This paper considers a situation called statistical privacy where an adversary knows the distribution by which the database is generated, but no exact data of all (or sufficient many) of its entries. We analyze in detail how the entropy of the distribution guarantes privacy for a large class of queries called property queries. Exact formulas are obtained for the privacy parameters. We analyze how they depend on the probability that an entry fulfills the property under investigation. These formulas turn out to be lengthy, but can be used for tight numerical approximations of the privacy parameters. Such estimations are necessary for applying privacy enhancing techniques in practice. For this statistical setting we further investigate the effect of adding noise or applying subsampling and the privacy utility tradeoff. The dependencies on the parameters are illustrated in detail by a series of plots. Finally, these results are compared to the differential privacy model.

cs.CR↗

arXiv2007

Knowledge State Algorithms: Randomization with Limited Information

We introduce the concept of knowledge states; many well-known algorithms can be viewed as knowledge state algorithms. The knowledge state approach can be used to to construct competitive randomized online algorithms and study the tradeoff between competitiveness and memory. A knowledge state simply states conditional obligations of an adversary, by fixing a work function, and gives a distribution for the algorithm. When a knowledge state algorithm receives a request, it then calculates one or more "subsequent" knowledge states, together with a probability of transition to each. The algorithm then uses randomization to select one of those subsequents to be the new knowledge state. We apply the method to the paging problem. We present optimally competitive algorithm for paging for the cases where the cache sizes are k=2 and k=3. These algorithms use only a very limited number of bookmarks.

cs.DS↗