Measuring Membership Privacy on Aggregate Location Time-Series

While location data is extremely valuable for various applications, disclosing it prompts serious threats to individuals' privacy. To limit such concerns, organizations often provide analysts with aggregate time-series that indicate, e.g., how many people are in a location at a time interval, rather than raw individual traces. In this paper, we perform a measurement study to understand Membership Inference Attacks (MIAs) on aggregate location time-series, where an adversary tries to infer whether a specific user contributed to the aggregates. We find that the volume of contributed data, as well as the regularity and particularity of users' mobility patterns, play a crucial role in the attack's success. We experiment with a wide range of defenses based on generalization, hiding, and perturbation, and evaluate their ability to thwart the attack vis-a-vis the utility loss they introduce for various mobility analytics tasks. Our results show that some defenses fail across the board, while others work for specific tasks on aggregate location time-series. For instance, suppressing small counts can be used for ranking hotspots, data generalization for forecasting traffic, hotspot discovery, and map inference, while sampling is effective for location labeling and anomaly detection when the dataset is sparse. Differentially private techniques provide reasonable accuracy only in very specific settings, e.g., discovering hotspots and forecasting their traffic, and more so when using weaker privacy notions like crowd-blending privacy. Overall, our measurements show that there does not exist a unique generic defense that can preserve the utility of the analytics for arbitrary applications, and provide useful insights regarding the disclosure of sanitized aggregate location time-series

First order plus frequency dependent delay modeling : new perspective or mathematical curiosity?

The first-order-plus-dead-time model (FOPDT) is a popular simplified representation of higher order dynamics. However, a well known drawback is the rapid decrease of the frequency response accuracy with increasing process order. This especially applies to the higher frequency range. Literature offers solutions by extending this three parameter model with more parameters. Here, a fractional dead time is proposed. As such, a Frequency-Dependent Delay (FDD) is introduced, which offers a better approximation. As the fractional-order term introduces nonlinear coupling between the phase and the magnitude of the process, the fitting of the function becomes an iterative process, so a constrained multi-objective optimization is needed. This novel model, first-order-plus-frequency-dependent-delay or FOPFDD is fitted on a real electrical ladder network of resistors and capacitors of four and eight parts. The classic model, which is clearly a special case of the new model, is outperformed in the entire bandwidth

Radial Spokes-A Snapshot of the Motility Regulation, Assembly, and Evolution of Cilia and Flagella

Propulsive forces generated by cilia and flagella are used in events that are critical for the thriving of diverse eukaryotic organisms in their environments. Despite distinctive strokes and regulations, the majority of them adopt the 9+2 axoneme that is believed to exist in the last eukaryotic common ancestor. Only a few outliers have opted for a simpler format that forsakes the signature radial spokes and the central pair apparatus, although both are unnecessary for force generation or rhythmicity. Extensive evidence has shown that they operate as an integral system for motility control. Recent studies have made remarkable progress on the radial spoke. This review will trace how the new structural, compositional, and evolutional insights pose significant implications on flagella biology and, conversely, ciliopathy

Application of a Mamdani-type fuzzy rule-based system to segment periventricular cerebral veins in susceptibility-weighted images

This paper presents an algorithm designed to segment veins in the periventricular region of the brain in susceptibility-weighted magnetic resonance images. The proposed algorithm is based on a Mamdani-type fuzzy rule-based system that enables enhancement of veins within periventricular regions of interest as the first step. Segmentation is achieved after determining the cut-off value providing the best trade-off between sensitivity and specificity to establish the suitability of each pixel to belong to a cerebral vein. Performance of the algorithm in susceptibility-weighted images acquired in healthy volunteers showed very good segmentation, with a small number of false positives. The results were not affected by small changes in the size and location of the regions of interest. The algorithm also enabled detection of differences in the visibility of periventricular veins between healthy subjects and multiple sclerosis patients. © Springer International Publishing Switzerland 2016.Postprint (author's final draft

Partition-dependent framing effects in lab and field prediction markets

Many psychology experiments show that individually judged probabilities of the same event can vary depending on the partition of the state space (a framing effect called "partition-dependence"). We show that these biases transfer to competitive prediction markets in which multiple informed traders are provided economic incentives to bet on their beliefs about events. We report results of a short controlled lab study, a longer field experiment (betting on the NBA playoffs and the FIFA World Cup), and naturally-occurring trading in macro-economic derivatives. The combined evidence suggests that partition-dependence can exist and persist in lab and field prediction markets

Beyond viral suppression: the quality of life of people living with HIV in Sweden

Sweden has one of the best HIV treatment outcomes in the world and an estimated 95% of all diagnosed people living with HIV are virally suppressed, but the quality of life (QoL) is understudied. The aim of this study was to examine the associations between variables within sociodemographic, behavioural, clinical, psychological, sexual life, social support and personal resource component and the QoL of people living with HIV in Sweden. Data were derived from a cross-sectional, nation-wide survey completed by 15% (n = 1096) of all people living with HIV and collected at 15 infectious disease clinics and 2 needle exchange sites during 2014. Ordinal univariate and multivariate logistic regression analyses were used to examine associations between potential contributors and QoL. Respondents reported high QoL: 63% rated their QoL 7 or higher on a scale ranging from 0 to 10. QoL was independent of gender, age, mode of HIV transmission and country of origin. Lower QoL was associated with recent homelessness, hazardous alcohol consumption, comorbidities, treatment side-effects, HIV-related physical symptoms, hopelessness, negative self-image, sexual dissatisfaction, and negative changes in sex life after HIV. The QoL of people living with HIV in Sweden was high overall, but still significantly influenced by HIV

Determining the Characteristic Vocabulary for a Specialized Dictionary using Word2vec and a Directed Crawler

Specialized dictionaries are used to understand concepts in specific domains, especially where those concepts are not part of the general vocabulary, or having meanings that differ from ordinary languages. The first step in creating a specialized dictionary involves detecting the characteristic vocabulary of the domain in question. Classical methods for detecting this vocabulary involve gathering a domain corpus, calculating statistics on the terms found there, and then comparing these statistics to a background or general language corpus. Terms which are found significantly more often in the specialized corpus than in the background corpus are candidates for the characteristic vocabulary of the domain. Here we present two tools, a directed crawler, and a distributional semantics package, that can be used together, circumventing the need of a background corpus. Both tools are available on the web

Genomic abundance is not predictive of tandem repeat localization in grass genomes.

Highly repetitive regions have historically posed a challenge when investigating sequence variation and content. High-throughput sequencing has enabled researchers to use whole-genome shotgun sequencing to estimate the abundance of repetitive sequence, and these methodologies have been recently applied to centromeres. Previous research has investigated variation in centromere repeats across eukaryotes, positing that the highest abundance tandem repeat in a genome is often the centromeric repeat. To test this assumption, we used shotgun sequencing and a bioinformatic pipeline to identify common tandem repeats across a number of grass species. We find that de novo assembly and subsequent abundance ranking of repeats can successfully identify tandem repeats with homology to known tandem repeats. Fluorescent in-situ hybridization shows that de novo assembly and ranking of repeats from non-model taxa identifies chromosome domains rich in tandem repeats both near pericentromeres and elsewhere in the genome