Improving Data Security for a Hybrid Society: Insights from New Study
Researchers develop a fast and efficient method for handling encrypted data in the cloud for Society 5.0
From financial transactions to the use of communication applications with artificial intelligence, our data is frequently transmitted from personal devices to the cloud. Handling this encrypted data in a secure but computationally efficient manner is becoming increasingly important in our data-driven society. Now, researchers from Tokyo University of Science develop a method that can perform computations with encrypted data faster and at a lower cost than conventional methods, while also improving security.
Society 5.0 envisions a connected society driven by data shared between people and artificial intelligence devices connected via the Internet of Things (IoT). While this can be beneficial, it is also essential to protect the privacy of data for secure processing, transmission, and storage. Currently, homomorphic encryption and secret sharing are two methods used to compute sensitive data while preserving its privacy.
Homomorphic encryption involves performing computations on encrypted data on a single server. While being a straightforward method, it is computationally intensive. On the other hand, secret sharing is a fast and computationally efficient way to handle encrypted data. In this method, the encrypted data or secret input is divided and distributed among multiple servers, each of which performs a computation such as multiplication with its piece of data. The results of these computations are then used to reconstruct the original data. In such a system, the secret can only be reconstructed if a certain number of pieces, known as the threshold, are available. Therefore, if the servers are managed by a single organization, there is a higher risk that the data could be compromised if the required number of pieces falls into the hands of an attacker.
To improve data security, it is ideal for multiple companies to manage computing servers in a decentralized manner such that each server is operated independently. This approach reduces the likelihood of an attacker gaining access to the threshold number of pieces required to reconstruct a secret. However, implementing this system can be challenging in practice due to the need for a fast communication network to allow geographically separated servers to communicate with each other.
This leads to an important question: is there a way to maintain data integrity without having to rely on independent servers, and without incurring a high computational cost?
In a study published on November 14, 2022, in Volume 10 of IEEE Access, Professor Keiichi Iwamura and Assistant Professor Ahmad A. Aminuddin of Tokyo University of Science, Japan, introduced a new secure computation method where all the computations are performed on a single server without a significant computational cost.
The system consists of a trusted third party (TTP), one computing server, four players who provide secret inputs to the server, and one player who restores the computation result. The TTP is a neutral organization that generates random numbers which are provided to the server (these are known as shares) and the players in certain combinations. These random numbers are used to encrypt the data. Each player then performs a computation with the random numbers and generates secret inputs which are sent to a server. The server then uses the shares and secret inputs, along with new values computed by the TTP, to perform a series of computations, the results of which are sent to a final player who reconstructs the computation result (Figure 1). This method allows for the decentralized computation of encrypted data while still performing the computation on a single server.
"In our proposed method, we realize the advantage of homomorphic encryption without the significant computational cost incurred by homomorphic encryption, thereby devising a way to securely handle data," says Prof. Iwamura, who led the study and is the paper's first author. Moreover, the method can also be modified such that the random numbers generated by the TTP can be stored securely by a Trusted Execution Environment (TEE), which is a secure area in a device's hardware (processor). As the TEE takes over the role of the TPP during the subsequent computational process, it reduces the communication time and improves the speed at which the encrypted data is handled.
As our society becomes more reliant on the internet, we are moving towards storing data on the cloud rather than locally. To securely manage the growing amount of data, it is important to have a reliable and efficient method of handling it. "We realize a method that addresses all the drawbacks of the aforementioned methods, and it is possible to realize faster and more secure computations than conventional methods using secret sharing," says Assistant Prof. Aminuddin. Here's to better data privacy in the future, thanks to research like this!
Image Title: The proposed flow of realizing secure computation
Image Caption: Researchers at the Tokyo University of Science use a combination of TTP and various players to encrypt the data. However, all the computation is performed on a single server.
Image Credit: Ahmad Akmal Aminuddin Mohd Kamal from Tokyo University of Science
License type: CC BY 4.0
|Title of original paper||:||TTP-Aided Secure Computation Using (k, n) Threshold Secret Sharing With a Single Computing Server|
Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan's development in science through inculcating the love for science in researchers, technicians, and educators.
With a mission of "Creating science and technology for the harmonious development of nature, human beings, and society," TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today's most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.
About Professor Keiichi Iwamura
from Tokyo University of Science
Keiichi Iwamura (Member, IEEE) received his Ph.D. from The University of Tokyo. From 1982 to 2006, he worked at Canon Inc. He is currently a Professor at Tokyo University of Science. His research interests include coding theory, information security, and digital watermarking. He is a fellow of the Information Processing Society of Japan and the Chairperson of the Technical Committee of Information Hiding and its Criteria for Evaluation and Technical Committee of Enriched Multimedia, Institute of Electronics, and Information and Communication Engineers, Japan.
About Assistant Professor Ahmad A. Aminuddin
from Tokyo University of Science
Ahmad Akmal Aminuddin Mohd Kamal (Member, IEEE) was born in Penang, Malaysia, in 1994. He received his B.S. and M.S. degrees in electrical engineering and Ph.D. in engineering from the Tokyo University of Science, Japan, in 2017, 2019, and 2022, respectively. He is currently an Assistant Professor with the Tokyo University of Science. His research interests include information security and multiparty computation using secret sharing and its application into searchable encryption.