IIITH-TM18004 PRIVACY AND SECURITY ON ONLINE SOCIAL MEDIA PROJECTS

What if you get to know that someone in Kimberly, Australia, knows where you went biking last Sunday? If this doesn’t creep you enough, what if the person also knows that you ran across the pavement in front of your house yesterday?  If you don’t come under the Unconcerned category of Westin’s  privacy segmentation , you must have guessed that we are hinting at the scary truth of the current day’s location privacy. With ubiquitous apps like Google(search), Facebook, Uber keeping records of your movements almost every second(with or without your permission), its hard to imagine how much location-based information do services like Google Maps, Foursquare, Strava posses. What our project is about? If these apps are collecting location-based data, then what do we, as students, got to do about it? Well, in this project, we didn’t do  any rocket science , we only took  publicly  available  routes  uploaded by a user ...

Problem statement The problem we try to address in this project is to classify a tweet as a either a Troll or an Organic Tweet .  So let's get started by defining what we define both of them. Troll We define troll as a opinion manipulating tweet that is posted by a malicious user for the sole intention of swinging the election results in favor or against a particular party. Organic Tweet?   Any tweet that is not a troll is a genuine / generic of organic tweet.  Motivation There are many popular cases where people are paid to make a post on public forums in order to change opinions of people in favour or against a particular party. One such popular example is Russian Troll Factory . In this case many people were paid to tweet on the subject of US Presidential Election 2016 which were about to happen at that time. We wanted to see if we can classify if a particular tweet as a paid opinion manipulating tweet or not. Data Col...

Motivation It is no secret that every image you post online contains locations information such as geotags, metadata , and device information. Tech-savvy users disable location tracking on their devices, remove metadata, and disable geotags before uploading images to protect their privacy. But it turns out that recent advancements in Deep Learning have gone one step further. It is now possible for a software to figure out your location given just the pixels in your image. Keeping this in mind, we asked the question, “What can users do now to protect themselves?” The answer was our project “UnLocate It”. Mission Create a tool that 1. Modifies your image AND 2. Fools these softwares WITH minimal loss in image quality The Pipeline Given an image, our software modifies the image in subtle ways, which are not visible to the human eye but completely baffle computers. What’s more, our neural network is fast and lightweight, an we envisage tha...