Tight Lower Bounds for Differentially Private Selection

A pervasive task in the differential privacy literature is to select the $k$ items of "highest quality" out of a set of $d$ items, where the quality of each item depends on a sensitive dataset that must be protected. Variants of this task arise naturally in fundamental problems like feature selection and hypothesis testing, and also as subroutines for many sophisticated differentially private algorithms. The standard approaches to these tasks---repeated use of the exponential mechanism or the sparse vector technique---approximately solve this problem given a dataset of $n = O(\sqrt{k}\log d)$ samples. We provide a tight lower bound for some very simple variants of the private selection problem. Our lower bound shows that a sample of size $n = \Omega(\sqrt{k} \log d)$ is required even to achieve a very minimal accuracy guarantee. Our results are based on an extension of the fingerprinting method to sparse selection problems. Previously, the fingerprinting method has been used to provide tight lower bounds for answering an entire set of $d$ queries, but often only some much smaller set of $k$ queries are relevant. Our extension allows us to prove lower bounds that depend on both the number of relevant queries and the total number of queries

The Limits of Post-Selection Generalization

While statistics and machine learning offers numerous methods for ensuring generalization, these methods often fail in the presence of adaptivity---the common practice in which the choice of analysis depends on previous interactions with the same dataset. A recent line of work has introduced powerful, general purpose algorithms that ensure post hoc generalization (also called robust or post-selection generalization), which says that, given the output of the algorithm, it is hard to find any statistic for which the data differs significantly from the population it came from. In this work we show several limitations on the power of algorithms satisfying post hoc generalization. First, we show a tight lower bound on the error of any algorithm that satisfies post hoc generalization and answers adaptively chosen statistical queries, showing a strong barrier to progress in post selection data analysis. Second, we show that post hoc generalization is not closed under composition, despite many examples of such algorithms exhibiting strong composition properties

gamification of farmer participatory priority setting in plant breeding design and validation of agroduos

ABSTRACTParticipatory methods to characterize farmers' needs and preferences play an important role in plant breeding to ensure that new varieties fulfill the needs and expectations of end users. Different farmer-participatory methods for priority setting exist, each one responding differently to trade-offs between various requirements, such as replicability, simplicity, or granularity of the results. All available methods, however, require training, academic skill, and staff time of specially qualified professionals. Breeding and variety replacement may be accelerated by empowering non-academic organizations, such as NGOs and farmer organizations, to carry out farmer-participatory priority setting. But for this use context, currently no suitable method is available. A new method is needed that demands relatively low skill levels from enumerators and respondents, engages farmers without the need for extrinsic incentives, and gives statistically robust results. To achieve these objectives, we followed prin..

Intervention options for small-scale family poultry development in south-eastern Madagascar: an expert survey

The diets of resource-poor farmers in the Atsimo Atsinanana (AA) region of south-eastern Madagascar have limited diversity and are low in animal protein. Although poultry farming is widespread, productivity is low, and consumption of eggs is uncommon. To enable effective development interventions targeting poultry value chains, this study pursues two goals: (i) to describe current challenges in small-scale poultry rearing and egg consumption in AA, and (ii) to explore viable options for promoting poultry production. We employ a survey approach, carrying out semi-structured interviews with 16 international and 12 local key informants (KIs) on small-scale poultry development. We find that poultry production in AA is critically constrained by high mortality due to diseases and predation, poor husbandry, and lack of veterinary services. The major health constraint is the Newcastle disease. Given the high mortality rates and low egg-laying potential of local chicken breeds, only small numbers of eggs are consumed, as farmers prioritise hatching. The main identified solutions include improvements in veterinary health and animal husbandry. KIs emphasised the development of animal health support services, including village vaccinators, upgrading feed with locally accessible protein sources, and the need for biosecure housing. Furthermore, training for farmers on poultry management, marketing, and vaccinations was suggested, in addition to creating awareness about the nutritional benefits of poultry products. Our findings are relevant to local development practitioners, as achieving food and nutrition security requires a multifaceted approach that fits local conditions. Our study provides actionable recommendations for improving small-scale family poultry production in AA.

Differentially Private Medians and Interior Points for Non-Pathological Data

We construct differentially private estimators with low sample complexity that estimate the median of an arbitrary distribution over $\mathbb{R}$ satisfying very mild moment conditions. Our result stands in contrast to the surprising negative result of Bun et al. (FOCS 2015) that showed there is no differentially private estimator with any finite sample complexity that returns any non-trivial approximation to the median of an arbitrary distribution