We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

In this article we show how to use some results of G. E. Murphy on the socalled standard basis of Hecke-Algebras of Type A to derive a similar basis for generalized Temperley-Lieb algebras. This standard basis is compared to the usual diagrammatic basis of the original Temperley-Lieb algebra used in knot theory and statistical physics.

No abstract

Recent databases are implemented as in-memory columnstores. Adjustable encryption offers a solution to encrypted database processing in the cloud.We show that the two technologies play well together by providing an analysis and prototype results that demonstrate the impact of mechanisms at the database side (dictionaries and their compression) and cryptographic mechanisms at the adjustable encryption side (order-preserving, homomorphic, deterministic and probabilistic encryption).

Abstract. In order to perform a join in a deterministically, adjustably encrypted database one has to re-encrypt at least one column. The problem is to select that column that will result in the minimum number of re-encryptions even under an unknown schedule of joins. Naive strategies may perform too many or even infinitely many re-encryptions. We provide two strategies that allow for a much better performance. In particular the asymptotic behavior is O(n 3/2 ) resp. O(n log n) re-encryptions for n columns. We show that there can be no algorithm better than O(n log n). We further extend our result to element-wise re-encryptions and show experimentally that our algorithm results in the optimal cost in 41% of the cases.