A Hybrid Approach to Privacy-Preserving Federated Learning

Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality during training processes does not provide sufficient privacy guarantees. Rather, we need a federated learning system capable of preventing inference over both the messages exchanged during training and the final trained model while ensuring the resulting model also has acceptable predictive accuracy. Existing federated learning approaches either use secure multiparty computation (SMC) which is vulnerable to inference or differential privacy which can lead to low accuracy given a large number of parties with relatively small amounts of data each. In this paper, we present an alternative approach that utilizes both differential privacy and SMC to balance these trade-offs. Combining differential privacy with secure multiparty computation enables us to reduce the growth of noise injection as the number of parties increases without sacrificing privacy while maintaining a pre-defined rate of trust. Our system is therefore a scalable approach that protects against inference threats and produces models with high accuracy. Additionally, our system can be used to train a variety of machine learning models, which we validate with experimental results on 3 different machine learning algorithms. Our experiments demonstrate that our approach out-performs state of the art solutions

Does GPS supervision of intimate partner violence defendants reduce pretrial misconduct? Evidence from a quasi-experimental study

Objectives This research examines the effect global positioning system (GPS) technology supervision has on pretrial misconduct for defendants facing intimate partner violence charges. Methods Drawing on data from one pretrial services division, a retrospective quasi-experimental design was constructed to examine failure to appear to court, failure to appear to meetings with pretrial services, and rearrest outcomes between defendants ordered to pretrial GPS supervision and a comparison group of defendants ordered to pretrial supervision without the use of monitoring technology. Cox regression models were used to assess differences between quasi-experimental conditions. To enhance internal validity and mitigate model dependence, we utilized and compared results across four counterfactual comparison groups (propensity score matching, Mahalanobis distance matching, inverse probability of treatment weighting, and marginal mean weighting through stratification). Results Pretrial GPS supervision was no more or less effective than traditional, non-technology based pretrial supervision in reducing the risk of failure to appear to court or the risk of rearrest. GPS supervision did reduce the risk of failing to appear to meetings with pretrial services staff. Conclusions The results suggest that GPS supervision may hold untapped case management benefits for pretrial probation officers, a pragmatic focus that may be overshadowed by efforts to mitigate the risk of pretrial misconduct. Further, the results contribute to ongoing discussions on bail reform, pretrial practice, and the movement to reduce local jail populations. Although the cost savings are not entirely clear, relatively higher risk defendants can be managed in the community and produce outcomes that are comparable to other defendants. The results also call into question the ability of matching procedures to construct appropriate counterfactuals in an era where risk assessment informs criminal justice decision-making. Weighting techniques outperformed matching strategies

Mathematical Achievement in Eighth Grade: Interstate and Racial Differences

The 1992 eighth grade mathematics test of the National Assessment of Educational Progress reveals a low average level of achievement, wide variation across states, and a large difference in average scores of white and black students. Multiple regression analysis across states indicates that the characteristics of children (such as readiness to learn in kindergarten) and of the households in which they live (such as mother's education) have much larger effects of NAEP test scores than do variables (such as the student/teacher ratio) that measure school characteristics. White-black differences in the levels of child and household variables account for much of the white- black difference in NAEP test scores.