CREDIT AND MORE INFOS:
https://www.biodesignchallenge.org/nyu-itp-2024
https://tisch.nyu.edu/itp/news/summer-2024/students-from-itp---ima-compete-in-biodesign-challenge
https://www.biodesignchallenge.org/nyu-itp-2024
https://tisch.nyu.edu/itp/news/summer-2024/students-from-itp---ima-compete-in-biodesign-challenge
Randomness is a fundamental characteristic of nature, embodying the inherent unpredictability of the world around us. It plays a crucial role in maintaining the complexity and resilience of natural systems, driving the processes of evolution and fostering diversity that governs life on earth.
As technology develops, humanity studies and adopts this randomness into diverse fields and nowadays, from our daily life to business, especially in secure encryption, the randomness works as a critical component, which scrambles data to ensure only authorized parties can understand the information. It is essential for protecting personal and group data in today's digital world. Then, how can we get random numbers?
In computer programming, pseudo-random number algorithms are commonly used to produce numbers that appear random. The “random()” function in most programming languages is an example.
The other way is extracting randomness from physical phenomena and incorporate it into computer systems.
The other way is extracting randomness from physical phenomena and incorporate it into computer systems.
We commonly use digitally generated random numbers however, one of the main problems of using the computer generated random numbers is that it's not truly random because it is predictably determined by initial seed values. Since the seed follows a set pattern, these encryption keys become predictable and repeatable, posing several significant problems in cryptography and data security, including vulnerabilities in Internet of Things devices, cost-effective attacks, and exposure to data breaches. These issues impact not only cybersecurity professionals but also all of us as internet users of the growing digital community.
As a solution, we studied and tried to find the true random numbers via physical world, nature.
There is a genus of bacteria called “Geobacter” in the soil. They are typically found in a variety of sedimentary environments where there is a lack of oxygen, and they are most commonly associated with environments rich in organic compounds and metals, such as heavy metal contaminated soil, groundwater, wetlands, and subsurface environments where they can reduce metals like iron and uranium.
More importantly, Geobacter species possess a unique metabolic process that helps precipitate metals in polluted sites while discharging electrons through their “nanowires” as a byproduct. This enables them not only to remediate metal and related contaminants for bioremediation, but also to generate electricity through Microbial Fuel Cells (MFCs), providing sustainable energy production.
As the generation of electricity by Geobacter is influenced by unpredictable environmental factors, we can detect these fluctuations and feed them into hardware devices to create high-entropy numerical sequences. These sequences, characterized by genuine randomness, are ideally suited for encryption and other applications requiring truly unpredictable data. In this way, we aimed to harness the physical process in nature and speculatively design a true random number generator.
Our project, Geobsecure, leverages the electrical current produced by the metabolic processes of Geobacter in soil. Combined with a programmed system that reads current changes at intervals of five seconds and converts them into six-digit numbers, we showcase a secure true random number generation system.
As a first step, we conducted extensive research, brainstorming, and discussions, studying the biology of Geobacter, microbial fuel cells (MFC), and encryption technologies. Through multiple iterations and valuable guidance from Dr. Ross McCree, we refined our concept into its current form.
Then we collected soil samples from Prospect Park. We dug holes at least six inches deep and underwater to obtain soil containing Geobacter and protected samples by concealing them from the oxygen.
Utilizing these soil samples, we created iterations of prototypes. We first experimented with Mudwatt, which harnesses the power of electricity-generating bacteria living in the soil to light up LEDs. Also, as seen in the middle image, we employed Mudwatt to measure the current generated via the anaerobic respiration of microbes. The signal was transmitted through the wire connecting to our circuit and ultimately generated the random numbers.
We then designed our larger 3D model, consisting of two primary components: the soil container and the base. The soil container is a transparent vessel shaped to resemble the body of Geobacter, intended to hold the soil sample. The base supports the soil container and stores essential components such as the microcontroller and display for current measurement.
For programming and data handling, the microcontroller is programmed to measure the current at intervals of five seconds. Readings are recorded with six decimal places to ensure precision.
Subsequently, a shuffling algorithm randomizes the six decimal places of each reading, generating a 6-digit number displayed on an LCD screen. Ideally, this data can be transmitted to servers for user access.
