The researchers used molybdenum disulfide, a 2D material, to create a low-power cryptographic chip less than one nanometer thick. The smart chip senses, stores, computes and secures data in one low-power platform.
“Information from our devices is currently stored in one location, the cloud, which is shared and stored in large servers,” says Saptarshi Das, Penn State associate professor of engineering science and mechanics, who also is affiliated with the Penn State School of Electrical Engineering and Computer Science, the Materials Research Institute and the College of Earth and Mineral Sciences’ Department of Materials Science and Engineering. “The security strategies employed to store this information are extremely energy inefficient and are vulnerable to data breaches and hacking.”
Cloud encryption is a current mode of security that converts data into a code to prevent unauthorized access. Popular messaging system WhatsApp, for example, uses the method, theoretically ensuring only the devices involved in the chat can access private messages. However, in practice, say the researchers, cloud encryptions are vulnerable to data leaks and are frequent targets for adversaries, according to researchers.
“Although software-based security modules are powerful, there exists a multitude of challenges with them,” says Akhil Dodda, a Penn State engineering science and mechanics doctoral student and first author of a paper of the research. “We developed a cryptographic platform using a two-dimensional material to overcome these security limitations.”
Silicon – the semiconductor material commonly used to make transistors in smartphones – would not work to build a transistor small enough to save on energy use, say the researchers. Instead, they turned to 2D materials, specifically molybdenum disulfide (MoS2), which is less than one nanometer thick, to create a low-power cryptographic chip. Together with collaborators the researchers synthesized the MoS2 needed to create the chip.
The chip employs 320 MoS2 transistors that each have a sensing unit, a storage unit and a computing unit to encrypt the data. To test the strength of the encryption process, the researchers used machine learning algorithms, which allowed them to study the output patterns and predict input information.
“We found that the advanced machine learning techniques couldn’t decode the encrypted information, reinforcing the resilience of the encryption process against machine learning attacks,” says Das. “Without prior knowledge of the information channels and decoding variables, it is extremely difficult to decode the information.”
In addition, say the researchers, the energy consumed in encrypting the information was significantly less than silicon-based security methods. The result was a low-power, all-in-one chip that could sense, store, compute and communicate information among connected devices — a potential solution for users who want added security but cannot afford to drain their handheld device batteries in day-to-day use.
“In the near future,” says Das, “we plan to reach out to federal agencies and private corporations who specialize in smart security to extend and expand the scope of our work.”