Location Privacy in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a new platform that engages individuals in collecting and analyzing environmental, social and other spatiotemporal information. With SC, requesters outsource their spatiotemporal tasks to a set of workers, who will perform the tasks by physically traveling to the tasks' locations. This chapter identifies privacy threats toward both workers and requesters during the two main phases of spatial crowdsourcing, tasking and reporting. Tasking is the process of identifying which tasks should be assigned to which workers. This process is handled by a spatial crowdsourcing server (SC-server). The latter phase is reporting, in which workers travel to the tasks' locations, complete the tasks and upload their reports to the SC-server. The challenge is to enable effective and efficient tasking as well as reporting in SC without disclosing the actual locations of workers (at least until they agree to perform a task) and the tasks themselves (at least to workers who are not assigned to those tasks). This chapter aims to provide an overview of the state-of-the-art in protecting users' location privacy in spatial crowdsourcing. We provide a comparative study of a diverse set of solutions in terms of task publishing modes (push vs. pull), problem focuses (tasking and reporting), threats (server, requester and worker), and underlying technical approaches (from pseudonymity, cloaking, and perturbation to exchange-based and encryption-based techniques). The strengths and drawbacks of the techniques are highlighted, leading to a discussion of open problems and future work

Machine Unlearning: A Survey

Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more. Yet a special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. At the same time, this ambitious problem has led to numerous research efforts aimed at confronting its challenges. To the best of our knowledge, no study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. Accordingly, with this survey, we aim to capture the key concepts of unlearning techniques. The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category's advantages and limitations. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities

Low temperature and temperature decline increase acute aortic dissection risk and burden: A nationwide case crossover analysis at hourly level among 40,270 patients.

Background: Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency with high mortality, so identifying modifiable risk factors of AAD is of great public health significance. The associations of non-optimal temperature and temperature variability with AAD onset and the disease burden have not been fully understood. Methods: We conducted a time-stratified case-crossover study using a nationwide registry dataset from 1,868 hospitals in 313 Chinese cities. Conditional logistic regression and distributed lag models were used to investigate associations of temperature and temperature changes between neighboring days (TCN) with the hourly AAD onset and calculate the attributable fractions. We also evaluated the heterogeneity of the associations. Findings: A total of 40,270 eligible AAD cases were included. The exposure-response curves for temperature and TCN with AAD onset risk were both inverse and approximately linear. The risks were present on the concurrent hour (for temperature) or day (for TCN) and lasted for almost 1 day. The cumulative relative risks of AAD were 1.027 and 1.026 per 1°C lower temperature and temperature decline between neighboring days, respectively. The associations were significant during the non-heating period, but were not present during the heating period in cities with central heating. 23.13% of AAD cases nationwide were attributable to low temperature and 1.58% were attributable to temperature decline from the previous day. Interpretation: This is the largest nationwide study demonstrating robust associations of low temperature and temperature decline with AAD onset. We, for the first time, calculated the corresponding disease burden and further showed that central heating may be a modifier for temperature-related AAD risk and burden. Funding: This work was supported by the National Natural Science Foundation of China (92043301 and 92143301), Shanghai International Science and Technology Partnership Project (No. 21230780200), the Medical Research Council-UK (MR/R013349/1), and the Natural Environment Research Council UK (NE/R009384/1)

Serum sTREM-1, PCT, CRP, Lac as biomarkers for death risk within 28 days in patients with severe sepsis

This study was undertaken to evaluate the clinical efficacy of serum soluble triggering receptors expressed by myeloid cell-1 (sTREM-1), procalcitonin (PCT), C-reactive protein (CRP) and lactic acid (Lac) as biomarkers for death risk within 28 days in patients with severe sepsis. Fifty-one cases of severe sepsis from the department of ICU in Lishui People’s Hospital from May 2013 to February 2017 were retrospectively analyzed. These cases were divided into survival (n=39) and death (n=12) groups based on the outcome within 28 days of treatment. Serum levels of sTREM-1, PCT, CRP and Lac were measured on the day of admission and compared between the survival and death groups. And the death prediction value within 28 days were evaluated according to serum sTREM-1, PCT, CRP and Lac. The serum level of TREM-1 and Lac were 128.70±46.10 pg/mL, 7.02±1.56 mmol/L for the death group and 83.69±26.57 pg/mL 4.44±0.45 mmol/L for survival group. The serum levels of sTREM-1 and Lac in death group were significantly higher than those of survival group (p0.05). The death prediction sensitivity, specificity and AUC within 28 days were high for serum sTREM-1 (75.00%, 77.78%, 0.79) and APACHEII (74.89%, 84.62%, 0.84). However, the prediction value of serum level PCT, CRP and Lac were relatively low. A significant positive correlation was found between serum sTREM-1 and APACHEII score rpearson =0.54, (p0.05)

Semantic analysis in location privacy preserving

With the increasing use of location-based services, location privacy has recently started raising serious concerns. Location perturbation and obfuscation are most widely used for location privacy preserving. To protect a user from being identified, a cloaked spatial region that contains other k - 1 nearest neighbors of the user is submitted to the location-based service provider, instead of the accurate position. In this paper, we consider the location-aware applications that services are different among regions. In such scenarios, the semantic distance between users should be considered besides the Euclidean distance for searching the neighbors of a user. We define a novel distance measurement that combines the semantic and the Euclidean distance to address the privacy-preserving issue in the aforementioned applications. We also present an algorithm kNNH to implement our proposed method. Moreover, we conduct performance study experiments on the proposed algorithm. The experimental results further suggest that the proposed distance metric and the algorithm can successfully retain the utility of the location services while preserving users\u27 privacy

A differentially private method for reward-based spatial crowdsourcing

The popularity of mobile devices such as smart phones and tablets has led to a growing use of spatial crowdsourcing in recent years. However, current solution requires the workers send their locations to a centralized server, which leads to a privacy threat. One of the key challenges of spatial crowdsourcing is to maximize the number of assigned tasks with workers’ location privacy preserved. In this paper, we focus on the reward-based spatial crowdsourcing and propose a two-stage method which consists of constructing a differentially private contour plot followed by task assignment with optimized-reward allocation. Experiments on real dataset demonstrate the availability of the proposed method

The prognostic value of serum PCT, hs-CRP, and IL-6 in patients with sepsis

To evaluate the prognostic value of serum procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), and interleukin-6 (IL-6) in patients with sepsis