Discovering private trajectories using background information

Trajectories are spatio-temporal traces of moving objects which contain valuable information to be harvested by spatio-temporal data mining techniques. Applications like city traffic planning, identification of evacuation routes, trend detection, and many more can benefit from trajectory mining. However, the trajectories of individuals often contain private and sensitive information, so anyone who possess trajectory data must take special care when disclosing this data. Removing identifiers from trajectories before the release is not effective against linkage type attacks, and rich sources of background information make it even worse. An alternative is to apply transformation techniques to map the given set of trajectories into another set where the distances are preserved. This way, the actual trajectories are not released, but the distance information can still be used for data mining techniques such as clustering. In this paper, we show that an unknown private trajectory can be reconstructed using the available background information together with the mutual distances released for data mining purposes. The background knowledge is in the form of known trajectories and extra information such as the speed limit. We provide analytical results which bound the number of the known trajectories needed to reconstruct private trajectories. Experiments performed on real trajectory data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds

Privacy risks in trajectory data publishing: reconstructing private trajectories from continuous properties

Location and time information about individuals can be captured through GPS devices, GSM phones, RFID tag readers, and by other similar means. Such data can be pre-processed to obtain trajectories which are sequences of spatio-temporal data points belonging to a moving object. Recently, advanced data mining techniques have been developed for extracting patterns from moving object trajectories to enable applications such as city traffic planning, identification of evacuation routes, trend detection, and many more. However, when special care is not taken, trajectories of individuals may also pose serious privacy risks even after they are de-identified or mapped into other forms. In this paper, we show that an unknown private trajectory can be reconstructed from knowledge of its properties released for data mining, which at first glance may not seem to pose any privacy threats. In particular, we propose a technique to demonstrate how private trajectories can be re-constructed from knowledge of their distances to a bounded set of known trajectories. Experiments performed on real data sets show that the number of known samples is surprisingly smaller than the actual theoretical bounds

Efficiently Reasoning with Interval Constraints in Forward Search Planning

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting

Spin-echo and diffusion-weighted MRI in differentiation between progressive massive fibrosis and lung cancer

PURPOSEWe aimed to investigate the value of magnetic resonance imaging (MRI)-based parameters in differentiating between progressive massive fibrosis (PMF) and lung cancer.METHODSThis retrospective study included 60 male patients (mean age, 67.0±9.0 years) with a history of more than 10 years working in underground coal mines who underwent 1.5 T MRI of thorax due to a lung nodule/mass suspicious for lung cancer on computed tomography. Thirty patients had PMF, and the remaining ones had lung cancer diagnosed histopathologically. The sequences were as follows: coronal single-shot turbo spin echo (SSH-TSE), axial T1- and T2-weighted spin-echo (SE), balanced turbo field echo, T1-weighted high-resolution isotropic volume excitation, free-breathing and respiratory triggered diffusion-weighted imaging (DWI). The patients’ demographics, lesion sizes, and MRI‐derived parameters were compared between the patients with PMF and lung cancer.RESULTSApparent diffusion coefficient (ADC) values of DWI and respiratory triggered DWI, signal intensities on T1-weighted SE, T2-weighted SE, and SSH-TSE imaging were found to be significantly different between the groups (p < 0.001, for all comparisons). Median ADC values of free-breathing DWI in patients with PMF and cancer were 1.25 (0.93–2.60) and 0.76 (0.53–1.00) (× 10-3 mm2/s), respectively. Most PMF lesions were predominantly iso- or hypointense on T1-weighted SE, T2-weighted SE, and SSH-TSE, while most malignant ones predominantly showed high signal intensity on these sequences.CONCLUSIONMRI study including SE imaging, specially T1-weighted SE imaging and ADC values of DWI can help to distinguish PMF from lung cancer

Rheological properties of asphaltite-water slurries

In this study, the rheological characteristics of asphaltite- water slurries ( AWSs) were investigated with respect to some of the most important parameters in the preparation of slurries. The effects of pulp density, chemical addition, pulp pH, and particle size on the rheological behavior and viscosity of AWSs were studied. The role of demineralization of asphaltite was also investigated, and rheological properties of raw and relatively demineralized asphaltites were compared. Results showed that viscosity of the AWSs was negatively influenced by increases in the pulp density and as the mean particle size decreased from 104.81 to 15.39 Am. Increases in pH provided reduced viscosity values. The effects of dispersing and stabilizing agents were studied with a chemical mixture including 90% polystyrene sulfonate ( PSS) as the dispersant and 10% Na- carboxylmethylcellulose ( Na- CMC) as the stabilizer. The change in the viscosity was also investigated as a function of the dosage of the chemical mixture used. Minimum viscosity was achieved with a 1.1% chemical mixture addition, while excess dosages resulted in adverse effects and thickening of the slurry. Studies with raw and demineralized samples showed that mineral matter and hydrophobic aggregation of particles are critical factors, significantly affecting the rheological characteristics of AWSs

Rapid progression of COVID-19 pneumonia to extensive fibrosis assessed with 3D volumetric CT

[No Abstract Available