Differentially Private Continual Releases of Streaming Frequency Moment Estimations

The streaming model of computation is a popular approach for working with large-scale data. In this setting, there is a stream of items and the goal is to compute the desired quantities (usually data statistics) while making a single pass through the stream and using as little space as possible. Motivated by the importance of data privacy, we develop differentially private streaming algorithms under the continual release setting, where the union of outputs of the algorithm at every timestamp must be differentially private. Specifically, we study the fundamental ?_p (p ? [0,+?)) frequency moment estimation problem under this setting, and give an ?-DP algorithm that achieves (1+?)-relative approximation (? ? ? (0,1)) with polylog(Tn) additive error and uses polylog(Tn)? max(1, n^{1-2/p}) space, where T is the length of the stream and n is the size of the universe of elements. Our space is near optimal up to poly-logarithmic factors even in the non-private setting. To obtain our results, we first reduce several primitives under the differentially private continual release model, such as counting distinct elements, heavy hitters and counting low frequency elements, to the simpler, counting/summing problems in the same setting. Based on these primitives, we develop a differentially private continual release level set estimation approach to address the ?_p frequency moment estimation problem. We also provide a simple extension of our results to the harder sliding window model, where the statistics must be maintained over the past W data items

SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution

Advanced text-to-image models such as DALL-E 2 and Midjourney possess the capacity to generate highly realistic images, raising significant concerns regarding the potential proliferation of unsafe content. This includes adult, violent, or deceptive imagery of political figures. Despite claims of rigorous safety mechanisms implemented in these models to restrict the generation of not-safe-for-work (NSFW) content, we successfully devise and exhibit the first prompt attacks on Midjourney, resulting in the production of abundant photorealistic NSFW images. We reveal the fundamental principles of such prompt attacks and suggest strategically substituting high-risk sections within a suspect prompt to evade closed-source safety measures. Our novel framework, SurrogatePrompt, systematically generates attack prompts, utilizing large language models, image-to-text, and image-to-image modules to automate attack prompt creation at scale. Evaluation results disclose an 88% success rate in bypassing Midjourney's proprietary safety filter with our attack prompts, leading to the generation of counterfeit images depicting political figures in violent scenarios. Both subjective and objective assessments validate that the images generated from our attack prompts present considerable safety hazards.Comment: 14 pages, 11 figure

Locate and Verify: A Two-Stream Network for Improved Deepfake Detection

Deepfake has taken the world by storm, triggering a trust crisis. Current deepfake detection methods are typically inadequate in generalizability, with a tendency to overfit to image contents such as the background, which are frequently occurring but relatively unimportant in the training dataset. Furthermore, current methods heavily rely on a few dominant forgery regions and may ignore other equally important regions, leading to inadequate uncovering of forgery cues. In this paper, we strive to address these shortcomings from three aspects: (1) We propose an innovative two-stream network that effectively enlarges the potential regions from which the model extracts forgery evidence. (2) We devise three functional modules to handle the multi-stream and multi-scale features in a collaborative learning scheme. (3) Confronted with the challenge of obtaining forgery annotations, we propose a Semi-supervised Patch Similarity Learning strategy to estimate patch-level forged location annotations. Empirically, our method demonstrates significantly improved robustness and generalizability, outperforming previous methods on six benchmarks, and improving the frame-level AUC on Deepfake Detection Challenge preview dataset from 0.797 to 0.835 and video-level AUC on CelebDF$\_$v1 dataset from 0.811 to 0.847. Our implementation is available at https://github.com/sccsok/Locate-and-Verify.Comment: 10 pages, 8 figures, 60 references. This paper has been accepted for ACM MM 202

Stroke Risk among Patients with Type 2 Diabetes Mellitus in Zhejiang: A Population-Based Prospective Study in China

Objective. This study aimed to explore the incidence of stroke and stroke subtypes among patients with type 2 diabetes mellitus (T2DM) based on the long-term surveillance data in Zhejiang, China, during 2007 to 2013. Materials and Methods. During January 1, 2007, and December 31, 2013, a total of 327,268 T2DM and 307,984 stroke patients were registered on Diabetes and Stroke Surveillance System, respectively. Stroke subtypes were classified according to standard definitions of subarachnoid hemorrhage, intracerebral hemorrhage, and ischemic stroke. The incidence of stroke and stroke subtypes was calculated by standardized incidence ratio (SIRs) with 95% confidence intervals (CIs) compared with general population. Results. The incidence of stroke and stroke subtypes among patients with T2DM was significantly higher than in general population. Stroke risk was found significantly increased with an SIR of 3.87 (95% CI 3.76–3.99) and 3.38 (95% CI 3.27–3.48) in females and males, respectively. The excess risk of stroke was mainly attributable to the significantly higher risk of cerebral infarctions with the risk for T2DM being four times that for general population. Conclusions. The relationship between stroke and T2DM was strong, especially in female. The incidence of stroke and stroke subtypes among patients with T2DM was up to 3-fold higher than in general population in Zhejiang province, especially the subtype of cerebral infarctions

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Electronic Cigarettes Use and Intention to Cigarette Smoking among Never-Smoking Adolescents and Young Adults: A Meta-Analysis

Electronic cigarettes (e-cigarettes) use is becoming increasingly common, especially among adolescents and young adults, and there is little evidence on the impact of e-cigarettes use on never-smokers. With a meta-analysis method, we explore the association between e-cigarettes use and smoking intention that predicts future cigarette smoking. Studies were identified by searching three databases up to January 2016. The meta-analysis results were presented as pooled odds ratio (OR) with 95% confidence interval (CI) calculated by a fixed-effects model. A total of six studies (91,051 participants, including 1452 with ever e-cigarettes use) were included in this meta-analysis study. We found that never-smoking adolescents and young adults who used e-cigarettes have more than 2 times increased odds of intention to cigarette smoking (OR = 2.21, 95% CI: 1.86–2.61) compared to those who never used, with low evidence of between-study heterogeneity (p = 0.28, I2 = 20.1%). Among never-smoking adolescents and young adults, e-cigarettes use was associated with increased smoking intention

Electronic Cigarettes Use and Intention to Cigarette Smoking among Never-Smoking Adolescents and Young Adults: A Meta-Analysis

Electronic cigarettes (e-cigarettes) use is becoming increasingly common, especially among adolescents and young adults, and there is little evidence on the impact of e-cigarettes use on never-smokers. With a meta-analysis method, we explore the association between e-cigarettes use and smoking intention that predicts future cigarette smoking. Studies were identified by searching three databases up to January 2016. The meta-analysis results were presented as pooled odds ratio (OR) with 95% confidence interval (CI) calculated by a fixed-effects model. A total of six studies (91,051 participants, including 1452 with ever e-cigarettes use) were included in this meta-analysis study. We found that never-smoking adolescents and young adults who used e-cigarettes have more than 2 times increased odds of intention to cigarette smoking (OR = 2.21, 95% CI: 1.86–2.61) compared to those who never used, with low evidence of between-study heterogeneity (p = 0.28, I2 = 20.1%). Among never-smoking adolescents and young adults, e-cigarettes use was associated with increased smoking intention

Association of 24-h urinary sodium excretion with microalbuminuria in a Chinese population

Abstract To assess the relationship of sodium, potassium and the ratio of sodium to potassium (Na/K) with albuminuria, a cross-sectional study was carried out in China in 2017. Sodium, potassium and albumin excretions were examined in a 24-h (h) urine sample collected from 1486 participants. Microalbuminuria was defined as 24-h urinary albumin excretion between 30 and 300 mg/24 h. The participants had an average age of 46.2 ± 14.1 years old, and 48.9% were men. The proportion of patients with microalbuminuria was 9.0%. As illustrated by the adjusted generalized linear mixed model, sodium concentration increased significantly with the increase in 24-h urinary albumin (β = 1.16, 95% confidence interval (CI) 0.38–1.93; P = 0.003). Multivariable-adjusted logistic regression analyses demonstrated that the odds ratio (OR) of microalbuminuria increased with the quartiles of sodium [OR = 2.20, 95% CI 1.26–3.84 (the maximum quartile vs. the minimum quartile), P for trend = 0.006]. Potassium and the Na/K ratio did not have any association with outcome indicators. A high amount of sodium intake was potentially correlated with early renal function impairment

The relationship between sleep duration and obesity risk among school students: a cross-sectional study in Zhejiang, China

Abstract Background Obesity has been identified as a major risk factor for a large number of chronic diseases. Understanding factors related to adolescent obesity is critical for prevention of chronic diseases. The associations between sleep duration and obesity among adolescents in the existing literature are controversial. Our study was designed to determine the prevalence of short sleep duration, and assess the association of sleep duration and obesity, among middle and high school students in Zhejiang, China. Methods 18,403 Students in 442 schools were recruited and surveyed using an anonymous, self-administered questionnaires. Weighted multivariable logistic regression models were used for data analyses. Results The mean (SD) age of the students was 15.9 (1.8) years. 49.7% of students were girls. The mean (SD) height and weight were 166.2 (8.5) cm and 54.6 (11.1) kg, respectively. The overall prevalence of obesity and overweight were 3.4% (95% CI: 3.0–3.8) and 7.8% (95% CI: 7.4–8.3), respectively. The overall prevalence of short sleep duration among students was 66.0% (95% CI: 63.8–68.1), higher among girls than boys (69.8% vs. 62.1%) (P < 0.0001). The figures for middle school, academic high school, and vocational high school were 59.0, 82.4 and 59.7%, respectively (P < 0.0001). As compared with girls who sleep 8 h per day (reference), the odds ratios (95% CI) of obesity for girls who sleep < 7 h, 7 h, 9 h and ≥ 10 h were 1.97 (1.15–3.38), 1.90 (1.18–3.04), 1.38 (0.86–2.20) and 2.12 (1.22–3.67) respectively, after adjustment for socio-demographic status, lifestyle factors, and mental health. The corresponding figures among boys were 1.45 (0.97–2.16), 1.13 (0.81–1.57), 1.25 (0.89–1.74), and 1.12 (0.81–1.54), respectively. Conclusions Insufficient sleep is prevalent among students in Zhejiang China. A U-shaped relationship was found between sleep duration and obesity risk among girls, with the lowest risk among those who slept for 8 h, but not among boys. Adequate sleep duration may be an important component of obesity prevention initiatives among adolescents

Association between multimorbidity and falls and fear of falling among older adults in eastern China: a cross-sectional study

BackgroundGrowing evidence has reported an association between multimorbidity and falls and fear of falling (FOF) in older adults, however, the results regarding this association from China are limited. Our study aimed to investigate the association between multimorbidity and falls and FOF in older adults in eastern China.MethodsWe conducted a cross-sectional study in Zhejiang Province, Eastern China, which recruited a provincial representative sample of adults aged ≥ 60 years. A structured questionnaire including demographic characteristics, chronic diseases, history of falls in the past 12 months, and FOF, was administered by all participants. The exposure variable was multimorbidity, which was defined as the presence of two or more chronic diseases and medical conditions in the same individual. The outcomes included a history of falls and FOF. Multivariate logistic regression was used to evaluate the association between multimorbidity and falls and FOF in older adults.ResultsIn total of 7,774 participants were included in the analysis, among whom 3,898 (50.1%) were female, with a mean ± standard deviation age is 72.9 ± 8.4 years. Multimorbidity was associated with the increased risk of falling in older adults [adjusted odds ratio (OR), 1.99; 95% confidence interval (CI):1.55–2.36]. The ORs for having experienced single fall and repeated falls were 1.85 (95% CI: 1.42–2.42) and 3.45 (95% CI: 1.47–6.97), respectively, with multimorbidity compared with those without chronic diseases. The older adults with multimorbidity were more likely to report FOF compared with those without chronic diseases (adjusted OR, 1.49; 95%CI:1.30–1.70). Moreover, the association between multimorbidity and FOF remained significant in the older adults with a history of fall (OR, 1.57; 95%CI:1.04–2.38).ConclusionThe association between multimorbidity and falls and FOF is significant in the Chinese population and the effects of multimorbidity on falls and FOF do not vary according to the frequency and history of falls in older adults