Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through the models' structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its influence, attribute inference attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks and begins to shed light on what other factors may be in play. Finally, we explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks

Treatment of International Human Rights Violations in the United States

INTRODUCTION: Health-related quality of life (HRQoL) in patients treated in intensive care has been reported to be lower compared with age- and sex-adjusted control groups. Our aim was to test whether stratifying for coexisting conditions would reduce observed differences in HRQoL between patients treated in the ICU and a control group from the normal population. We also wanted to characterize the ICU patients with the lowest HRQoL within these strata. METHODS: We did a cross-sectional comparison of scores of the short-form health survey (SF-36) questionnaire in a multicenter study of patients treated in the ICU (n = 780) and those from a local public health survey (n = 6,093). Analyses were in both groups adjusted for age and sex, and data stratified for coexisting conditions. Within each stratum, patients with low scores (below -2 SD of the control group) were identified and characterized. RESULTS: After adjustment, there were minor and insignificant differences in mean SF-36 scores between patients and controls. Eight (n = 18) and 22% (n = 51) of the patients had low scores (-2 SD of the control group) in the physical and mental dimensions of SF-36, respectively. Patients with low scores were usually male, single, on sick leave before admission to critical care, and survived a shorter time after being in ICU. CONCLUSIONS: After adjusting for age, sex, and coexisting conditions, mean HRQoL scores were almost equal in patients and controls. Up to 22% (n = 51) of the patients had, however, a poor quality of life as compared with the controls (-2 SD). This group, which more often consisted of single men, individuals who were on sick leave before admission to the ICU, had an increased mortality after ICU. This group should be a target for future support

Restricted-Feeding Versus Self-Feeding Sows During Pregnancy

An experiment was designed to compare the performance of sows during pregnancy when self- fed a bulky ration or limit-fed a higher energy ration. In the fall of 1964 , second litter sows were self-fed and housed together during the breeding period. After the breeding period, the sows were divided and one group was self-fed and the other group was limit- fed 5 lb. per head daily. Limit-fed sows were given their feed once daily in open troughs. Self-fed sows had access to a round metal fee der at all times. Water was fed ad libitum and pasture was available to both groups