Efficient subgroup exponentiation in quadratic and sixth degree extensions

This paper describes several speedups for computation in the order p + 1 subgroup of F*(p2) and the order p2 - p + 1 subgroup of F*(p6). These results are in a way complementary to LUC and XTR, where computations in these groups are sped up using trace maps. As a side result, we present an efficient method for XTR with p ≡ 3 mod

Discrete logarithm variants of VSH

Recent attacks on standardised hash functions such as SHA1 have reawakened interest in design strategies based on techniques common in provable security. In presenting the VSH hash function, a design based on RSA-like modular exponentiation, the authors introduce VSH-DL, a design based on exponentiation in DLP-based groups. In this article we explore a variant of VSH-DL that is based on cyclotomic subgroups of finite fields; we show that one can trade-off performance against bandwidth by using known techniques in such groups. Further, we investigate a variant of VSH-DL based on elliptic curves and extract a tighter reduction to the underlying DLP in comparison to the original VSH-DL proposa

Experiments Participation Act:Know what works for whom

In recent years (1 October 2017 - 31 December 2019), six municipalities have conducted experiments in the assistance under the Temporary Decree Experiments Participation Act. The aim was to improve the implementation of the Participation Act through less control and coercion and more room for own direction, extra guidance and customization. The municipalities have asked the research institutes to scientifically investigate the results of these experiments. In order to be able to compare, the researchers used the same questionnaire in all six municipalities, the same outcome measures were used and the analysis model is also the same. However, because the experiments are designed at the municipal level, the differences between the municipalities and the experiments implemented are too big to really be able to combine the data. For a complete comparison and interpretation of the six experiments, we refer to the local final reports. The experiments, however, each individually, but above all in conjunction, provide relevant information for (future) policy that can fuel the discussion about the future of social security in the Netherlands. That is why we decided to write this joint semi-trailer to provide an initial insight into the broader outcomes on a selection of outcome measures. After a brief summary of the conclusions, we will discuss the various experiments and some important outcome measures in more detail

Addressing unanticipated interactions in risk equalization : a machine learning approach to modeling medical expenditure risk

Abstract: Adverse selection harms market efficiency and access to essential services, particularly for disadvantaged groups. Risk equalization policies attempt to mitigate this by compensating agents for risk disparities, but often fall short of addressing interactions between risk factors. Using health insurance data from the Netherlands, we present a machine learning approach to capture unanticipated interactions that impact medical expenditure risk. We compare our novel approach to a state-of-the-art statistical model. We find that our approach explains an additional 1.5% of medical expenditure, equivalent to 571 million euros over all individuals in the Dutch market. In particular, this translates into better compensation for low-and high-cost groups that are especially vulnerable to adverse selection. These findings confirm the significance of risk factor interactions in explaining medical expenditure risk, and support the adoption of machine learning alongside statistical models to further mitigate selection incentives in risk equalization policies