Representative Publication

This work initiates the research on finding a secure cryptographic tool enabling efficient comparison operation over encrypted data. The paper has been cited more than 1200 times according to Google Scholar. (Thanks to Prof. Sasha for the image)

Statistical analysis of largescale data is useful as it enables the extraction of a large amount of information, despite its simplicity. Therefore, fusing and analyzing data from different security domains is an attractive and promising approach, unless it jeopardizes the privacy of the data in any security domain. In this study, we proposed the HEaaN-STAT toolkit that can efficiently fuse data from different domains to enable largescale statistical analysis while protecting data privacy. Moreover, we proposed an efficient inverse operation and a table lookup function for Cheon-Kim-Kim-Song (CKKS) encrypted data, as well as a data encoding method for counting encrypted data. Based on this, we proposed a method for generating a contingency table with a large number of cases and k-percentile for largescale data that is hundreds to thousands of times faster than the method proposed by Lu et al. in NDSS’ 17. The validity of the proposed toolkit was verified through practical use for business applications using real-world data. 

 The initial version of HEaaN.stat (https://heaan.it/)  was developed based on the techniques shown in the paper.  

We have demonstrated that very fast CKKS HE operations are possible including the bootrapping operation with the help of GPU. We also have figured out the importance of the memory bandwitdh for efficient HE operations. 

We have found an efficient sorting method over encrypted data by applying k-way sorting network to the encrypted data by CKKS. With the help of efficient data comparison method and SIMD operations supported by CKKS HE, we could sort tens of thousands of data very quickly.

Making deep neural networks available as a service introduces privacy problems, for which homomorphic encryption of both model and user data potentially offers the best solution. However, the difficulty of operating on homomorphically encrypted data has hither to limited the range of operations available and the depth of networks. We introduce an extended CKKS scheme MatHEAAN to provide efficient matrix representations and operations together with improved noise control. Using the MatHEAAN we developed a deep sequential model with a gated recurrent unit called MatHE-GRU. We evaluated the proposed model using sequence modeling, regression, and classification of images and genome sequences. We show that the hidden states of the encrypted model, as well as the results, are consistent with a plaintext model.

The author in this work has found a very simple and efficient way to provide order-comparison operation over encrypted data while strengthening the level of security compared to the existing works.  The proposed work utilizes a simple bucketing approach that is a common method for securing database systems, as well as, reducing the complexity of the data to enable efficient search operations.

We have discovered a security vulnerability in the Samsung Pay app. We could successfully eavesdropp the one-time token for a payment made on the Samsung Pay app around 0.6m ~ 2.0m from where the payment was taking place, and have verified that the collected one-time token could be used away from the victim device after transmitting the collected payment information over the Internet.