It’s coming.

It bided its time infecting camels.

But now the demon has escaped. A mutation has set the virus free. 

And it is here!…the Middle Eastern Respiratory Syndrome! (MERS).

Millions will fall prey to it merciless infection, civilization will collapse, and The Walking Dead will experience a revival as a sitcom.

 Well, maybe not.

Actually, the World Health Organization declared that the virus is not (yet) a Public Health Emergency of International Concern. So hold off on the panic. In the mean time, here’s a handy MERS fact sheet:

What is it?

Middle Eastern Respiratory Syndrome, or MERS, is a type of coronavirus. The coronavirus family also includes Severe Acute Respiratory Syndrome (SARS) virus that caused a worldwide outbreak in 2003.  

Less dramatically, coronaviruses are a major cause of the common cold. 

Where did it come from?

MERS was first diagnosed in Jordan and Saudi Arabia, back in 2012.

Why should we be scared of MERS?

About 25% of patients infected with MERS die.

Since MERS is a virus, it cannot be treated with antibiotics. So far, there is no vaccine. 

MERS bears some similarities to SARS, which killed hundreds of people in 2003.

More than one hundred people have already died of MERS. Two cases have been identified in the United States. 

Why not panic? 

MERS does not pass easily between humans. It is certainly less contagious than SARS.  

According to Dr. Ann Schuchat, director of the CDC’s National Center for Immunization and Respiratory Diseases, the 

“Risk to the general public remains very low.”

Roll credits. 

PhD Dissertation Proposal

Learning and Executing Energy Efficient, Context-Dependent
Rules for Privacy and Security Management on Mobile Devices

Prajit Kumar Das

8:00am Friday, 16 May 2014, ITE325b

There are ongoing security and privacy concerns around mobile platforms that are increasingly being used by citizens. For example a newly discovered security flaw in WhatsApp that allows hackers using a malicious app to read chat messages stored on the SD card. The Brightest Flashlight application was reported to have logged precise location and a unique user identifier, which have nothing to with its intended functionality. Current mobile platform privacy and security mechanisms are limited to an initial installation phase permission acquisition method. In addition to that, the permissions are of the all or none form. This means that either the users accept all the permissions requested by the mobile app or they cannot use the app in question. Even if permissions were not structured as such, typically, users do not understand the permissions being requested or are too eager to use the application to even care to read them. These issues are present in all major mobile operating systems. Given the penetration of mobile devices into our lives, a fine-grained context-dependent security and privacy control approach needs to be created.

We propose a framework that will allow us to learn the privacy and security rules for a particular user, on their mobile devices. We do this by employing a simple user feedback mechanism. The rule learning framework consists of a “learning mode” where it observes and learns from user behavior and a “working mode” where it implements the learned rules to protect user privacy and provide security. The rules are represented to the user in plain English using an easily understandable construct. The rules are internally written in a logic based language and using Semantic Web technologies. The antecedents of the rules are context elements that are derived from an ontology using a query engine and an inference mechanism. The main contributions of our work include learning modifications to current rules and learning new rules to control the data flow between the various data providers on the user’s mobile device, including sensors and services and the consumer of such data. The privacy and security rule execution consumes significant energy due to the context detection. We create an energy model that allows us to make energy cost optimizations with regards to rule execution. We use a three-fold solution for achieving the said energy cost optimizations.

Committee: Drs. Anupam Joshi (chair), Nilanjan Banerjee, Dipanjan Chakraborty (IBM), Tim Finin, Tim Oates, Arkady Zaslavsky (CSIRO)

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