Private location centric profiles for GeoSocial networks

Author(s):  
Bogdan Carbunar ◽  
Mahmudur Rahman ◽  
Naphtali Rishe ◽  
Jaime Ballesteros
2019 ◽  
Vol 6 (3) ◽  
pp. 4472-4481 ◽  
Author(s):  
Lu Zhou ◽  
Le Yu ◽  
Suguo Du ◽  
Haojin Zhu ◽  
Cailian Chen

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ante Dagelić ◽  
Toni Perković ◽  
Bojan Vujatović ◽  
Mario Čagalj

User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.


Circulation ◽  
2019 ◽  
Vol 140 (Suppl_2) ◽  
Author(s):  
Brian E Grunau ◽  
Emad Awad ◽  
Takahisa Kawano ◽  
Frank Scheuermeyer ◽  
Robert Stenstrom ◽  
...  

Introduction: It is unclear if the benefits of public access defibrillator (PAD) programs are similar between men and women. We investigated the location of out-of-hospital cardiac arrests (OHCA) stratified by sex to determine what proportion was eligible for PAD application. Second, we sought to determine if patient sex was associated with PAD utilization. Methods: We analyzed prospectively collected data from the North American Resuscitation Outcomes Consortium (ROC) Epistry dataset (2011 - 2015), excluding emergency medical services (EMS)-witnessed cases, those not treated by EMS, and children aged less than 10. We compared sex-based differences in public vs private location, and location type (street or highway, public building, place of recreation, industrial place, home residence, farm or ranch, healthcare facility, residential institution, other public property, or other private location). Among public location OHCAs with bystander interventions, we fit an adjusted logistic regression model to estimate the association between sex and PAD application. Results: Among the 61,473 cases, 20,933 (34%) were female, 30,353 had resuscitation attempted by bystander, and 13,597 had initial shockable rhythms. The OHCA incidence in a public location for women and men was 8.8% and 18%, respectively (95% CI for difference 8.7 - 9.7). Women had a significantly lower proportion of OHCAs on the street/highway, in public buildings, places of recreation, and farms, but a significantly higher proportion in the home, healthcare facilities, and residential institutions. Among public location OHCAs with bystander interventions, female sex was associated with a lower odds of bystander PAD application (adjusted OR 0.83, 95% CI 0.70-0.99). Conclusion: Women had fewer OHCAs in public locations eligible for PAD application. Further, among public OHCAs with bystander interventions, women were less likely to have PADs applied.


Author(s):  
Bogdan Carbunar ◽  
Radu Sion ◽  
Rahul Potharaju ◽  
Moussa Ehsan
Keyword(s):  

Author(s):  
Muhammad Mazhar Ullah Rathore ◽  
Awais Ahmad ◽  
Anand Paul

Geosocial network data provides the full information on current trends in human, their behaviors, their living style, the incidents and events, the disasters, current medical infection, and much more with respect to locations. Hence, the current geosocial media can work as a data asset for facilitating the national and the government itself by analyzing the geosocial data at real-time. However, there are millions of geosocial network users, who generates terabytes of heterogeneous data with a variety of information every day with high-speed, termed as Big Data. Analyzing such big amount of data and making real-time decisions is an inspiring task. Therefore, this book chapter discusses the exploration of geosocial networks. A system architecture is discussed and implemented in a real-time environment in order to process the abundant amount of various social network data to monitor the earth events, incidents, medical diseases, user trends and thoughts to make future real-time decisions as well as future planning.


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