Introduced by Shomit Ghose, Data-X Advisor
This revolves around the project of creating a code set and experiment to see what level of “fake and automated” requests on internet sites would have the effect of confusing the AI algorithms that track users preferences. This can be useful to users who want to have increased privacy, and the results can be useful to firms who would want to understand what information would still be reliable about customers if the data collected becomes increasingly noisy.
Data Engineering via Noise Injection
Today, online data trails provide large volumes of private, real-time consumer information to companies doing business on the Internet. Whether it’s search histories, GPS locations, browsing behavior, or social media content, companies such as Google, Amazon and Facebook are able to mine data streams to gain insights about details that consumers may consider private (and may incorrectly assume is undiscoverable). The Noise Injection project explores methods by which an individual’s data streams can be obfuscated or rendered statistically invalid via the injection of irrelevant data into an existing data stream. The project serves to explore the methods by which the data on which machines are trained can be engineered to invalidate the training. This serves the purpose of both building a mechanism of delivering some amount of Internet privacy to the individual, as well as provide an understanding of how data engineering attacks can be executed so that methods can in turn be developed to defeat malicious attacks by bad-actors.