CP Analyzer: Extract the coding habits of students

  


Introduction

Nowadays, its a very common sight that students put their online coding plat orms handle in their resume while applying for a job/internship in various companies. but there could be ways in which this information of the candidate could be used against him to carry out the information about the candidate that he might not want to be disclosed to the interviewer of the company. This exploitation of the privacy and carrying out of the information of the candidate is also called as Inference Attack, that is a type of attack in which a person’s sensitive information is inferred by the data disclosed by him. In this project, we took the example of Codeforces which is one of the most used platform for online coding competitions and coding practice.

Implementation

Data Fetching

Codeforces provides a variety of APIs. One of them was designed to fetch all the submissions of a user. The result of the API for a user is given below.


Data Interpretation

Now that we have all the information about all the submissions of that user we need to carry out various information out of it. Such as, the number of problems solved by the user in each language, like 40 problems in C++, 50 problems solved by the user in python etc. This information helps the interviewer or any adversary to find the strongest and weakest languages of that user. Secondly, the count of the problems solved by the user accoding to the rating of that problem. In codeforces, the difficulty level of the problems are labeled using a rating tag. This info could be used to infer the problem solving skills level of that user like how much difficult and how much easy-medium level problems are there that this
user can solve. And the most important, the sorting of the problems according to their topic. For example, the user solved 70 problems on graph and among these 70, the ratings of each problem so that the interviewer can get insight about every topic that the user has solved and also the count of the problems in each category that shows the number of Wrong submissions, number of submissions that gave TLE, number of submissions that gave Runtime error etc. Last but not the least, the list of problems in every topic that the user was not able to solve. These are the problems that user tried and got wrong submission and was not able to do these problems till date.

Results

After fetching all the results we plotted graphs of various kinds so that the interviewer could infer all the coding habits of the user at a glance. To showcase the results, we have taken an example user tourist who is a very famous coder on codeforces.




Conclusion and Mitigation

In conclusion, The Inference Attack was successfully done on the codeforces platform. This could be scaled up to other platforms like codechef, atcoder etc. In order to mitigate this, the platform should provide a facility to the users to chose the visibilty of their submissions. That is, the User should have liberty to make their submissions public or private.

Video presentation





The Team

  • Danial Kafeel
  • Janmejay Pratap Singh Baghel
  • Akshay M
  • Divya Donapati
  • Chirag Shilwant

Acknowledgement

This project was carried out as part of the course Online Privacy , under the guidance of Professor Ponnurangam Kumaraguru, at International Institute of Information Technology, Hyderabad.

Popular posts from this blog

Applications of NLP in Privacy Policies

Image
  Photo by Marija Zaric on Unsplash How many privacy policies have you accepted without knowing what the policy states? We're guessing that number is probably pretty high. And you are not alone. A  survey  of 2100 individuals conducted reported that 87% of individuals accept policies without reading them. Common reasons for this included the length and general readability of the policies. Of the top 500 websites, the average reading score was comparable to that of a high school student , while the average reading level of US adults is that of the 7th grade. Luckily, Natural Language Processing (NLP) can help. For our project, we set out to detect the readability of current privacy policies (spoiler alert: you'd need to have at least completed high school to make sense of what you agree to). We also wanted to see how we could make the readability of the policies better. So what did we do with the privacy policies? We used privacy policies from the  OPP-115 Corpus ...
Read more

"TL;DR: No more an excuse!": On making privacy policies easier to read and interpret

Image
Introduction Many websites collect, share, and use users’ Personally Identifiable Information (PII). Many regulatory bodies around the globe have long enforced requirements on posting privacy policies online. Over the past few decades, however, the collection, sharing, and usage of users' PII have risen to a major privacy concern over the internet, so much so that newer laws have gone into effect to protect user privacy. Prominent examples of such laws are the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States. Research has shown, time and again, that privacy policies are too long and hard to comprehend for their intended users, and therefore users rarely take the time and effort to read them. Privacy Policies are getting longer over time and increasingly difficult to read. To address the poor readability of privacy policies, we have developed tools that leverage machine learning (ML) to automatically s...
Read more

How private is the Instagram Private mode?

Image
  Introduction: In today's world maintaining a social media presence is just as important as maintaining a resume. With Globalization, comes the need of fast and reliable communication and social media platforms like Twitter, Facebook, Instagram, LinkedIn, etc are quick to fulfill this need, thus creating, a still expanding, market of people's identity, expressions and emotions with the help of computer algorithms, powerful processors and mighty memory units.  "Why call it a market?", One might ask, and rightfully so. Such a person must understand that despite the wholesome implications of bringing families and friends together, Social Media platforms are businesses run by big corporations. They run on advertisers money (among other things) and their ultimate goal is to rake in more and more profit. And a business whose most defining feature is to collect, store and display the private and sensitive data of billions of people, must implement at least som...
Read more