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Exploring New Methods to Enhance Data Privacy

Challenge: Can we protect the privacy of individual consumers by injecting irrelevant or misdirected noise into the Internet data stream?

Today, online data trails provide large volumes of private, real-time consumer information to companies doing business on the Internet.  Search histories, GPS locations, browsing behaviors or social media content postings, allow companies like Google, Amazon and Facebook to mine data streams to gain insights and details that consumers may consider private (and may incorrectly assume is undiscoverable).  Can we protect the privacy of individual consumers by injecting irrelevant or misdirected noise into the Internet data stream?

Application Date

Monday, January 1st, 2018

Kickoff Date

Monday, January 1st, 2018

Final Presentation Date

Monday, January 1st, 2018


“Noise Injection” seeks to explore 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 providing 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.


Discover and define mechanisms by which Internet activity signatures can be statistically flooded.  A practical implementation would be to build a browser extension which randomly generates product search activity on Amazon in a way that mimics human browsing patterns, or a browser extension that generates random (but plausible) Google searches. Also, comprehensively discover and define the best practices through which Internet activity signatures can be simply erased: cookie cleaning, browser history cleaning, switching off GPS, etc.


Shomit Ghose - Managing Director & Partner at Onset Ventures - Shomit is a longtime Silicon Valley entrepreneur with deep experience in software start-ups, both as a venture capitalist / board member, and as an operating executive. Multiple successful IPOs as an operating exec, and multiple successful exits-by-acquisition as both an operating exec and as a board member. Started entrepreneurial life as a UC Berkeley-trained software engineer, and currently a managing director and partner at ONSET Ventures."


Graduate and undergraduate students (Juniors & Seniors) in data science, computer science, statistics, IEOR, math, and economics are encouraged to apply. This is a team project and teams will be pre-formed by the project leaders from the pool of eligible students.


Kick-off: Wednesday, September 20th 4:30 pm - 6 pm
Midpoint Check in: Wednesday, October 11th - 4:30 pm - 6 pm
Check in: Wednesday, November 8th - 4:30pm - 6 pm
Final Presentation: Monday, November 27th 4:30 pm - 6 pm


Interested students should follow the "Apply Now" button at the top of the page.
Notifications of acceptances will be sent by September 14th.
Deadline to apply is Sunday, September 10th.

Students can take this Collider for 2 units credit:
INDENG 190C Advanced Topics: Innovation Collider
Class # 39423
Academic Units: 2
Pass/No Pass

Note: If you registered for INDENG 190C and are not eligible or cannot participate in the project, you will be required to drop the course.

For additional questions, please email

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