Added oblivious games

README.md

@@ -90,6 +90,12 @@ Federated learning involves training statistical models over remote devices or s
 
 ### Games & Simulations
 
+* [DP Vision](https://www.oblivious.com/games/dp-vision) - Test your image recognition skills with differentially private images! Players manage a privacy budget to reveal image details, aiming to identify the correct image within 5 guesses while minimizing privacy loss.
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+* [Guess Who (DP Edition)](https://www.oblivious.com/games/guess-who) - A privacy-preserving twist on the classic game where players ask yes/no questions with adjustable accuracy levels. Lower epsilon means less reliable but more private answers, teaching the privacy-utility tradeoff.
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+* [WORDPL](https://www.oblivious.com/games/wordpl) - A Wordle-style game with differential privacy mechanics. Players guess 5-letter words while managing privacy budgets that affect the accuracy of feedback, demonstrating how DP noise impacts information gathering.
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 * [Federated Learning Hyperparam Tuning Game](https://amanpriyanshu.github.io/FL-Interactive-Game/) - Understand and play with federated learning hyperparams! In-browser tensorflow-js simulation of FedAvg to understand and gain intuition about IID and Non-IID Federated Learning settings.
 
 * [Differentially Private Tetris](https://amanpriyanshu.github.io/Differentially-Private-Tetris/) - A unique twist on classic Tetris where players manage a privacy budget to reveal blocks, demonstrating differential privacy concepts through gameplay. Experience privacy-utility tradeoffs in an engaging way.