Efficient Keyset Design for Neural Networks Using Homomorphic Encryption
With the advent of the Internet of Things (IoT), large volumes of sensitive data are produced from IoT devices, driving the adoption of Machine Learning as a Service (MLaaS) to overcome their limited computational resources. However, as privacy concerns in MLaaS grow, the demand for Privacy-Preservi...
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4320 |
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| author | Youyeon Joo Seungjin Ha Hyunyoung Oh Yunheung Paek |
| author_facet | Youyeon Joo Seungjin Ha Hyunyoung Oh Yunheung Paek |
| author_sort | Youyeon Joo |
| collection | DOAJ |
| description | With the advent of the Internet of Things (IoT), large volumes of sensitive data are produced from IoT devices, driving the adoption of Machine Learning as a Service (MLaaS) to overcome their limited computational resources. However, as privacy concerns in MLaaS grow, the demand for Privacy-Preserving Machine Learning (PPML) has increased. Fully Homomorphic Encryption (FHE) offers a promising solution by enabling computations on encrypted data without exposing the raw data. However, FHE-based neural network inference suffers from substantial overhead due to expensive primitive operations, such as ciphertext rotation and bootstrapping. While previous research has primarily focused on optimizing the efficiency of these computations, our work takes a different approach by concentrating on the rotation keyset design, a pre-generated data structure prepared before execution. We systematically explore three key design spaces (KDS) that influence rotation keyset design and propose an optimized keyset that reduces both computational overhead and memory consumption. To demonstrate the effectiveness of our new KDS design, we present two case studies that achieve up to 11.29× memory reduction and 1.67–2.55× speedup, highlighting the benefits of our optimized keyset. |
| format | Article |
| id | doaj-art-01f34bd4de4446c1a8a88dcb854428e4 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-01f34bd4de4446c1a8a88dcb854428e42025-08-20T02:47:21ZengMDPI AGSensors1424-82202025-07-012514432010.3390/s25144320Efficient Keyset Design for Neural Networks Using Homomorphic EncryptionYouyeon Joo0Seungjin Ha1Hyunyoung Oh2Yunheung Paek3Department of Electrical and Computer Engineering & ISRC, Seoul National University, Seoul 08826, Republic of KoreaDepartment of Electrical and Computer Engineering & ISRC, Seoul National University, Seoul 08826, Republic of KoreaDepartment of AI, Gachon University, Seongnam-si 13120, Republic of KoreaDepartment of Electrical and Computer Engineering & ISRC, Seoul National University, Seoul 08826, Republic of KoreaWith the advent of the Internet of Things (IoT), large volumes of sensitive data are produced from IoT devices, driving the adoption of Machine Learning as a Service (MLaaS) to overcome their limited computational resources. However, as privacy concerns in MLaaS grow, the demand for Privacy-Preserving Machine Learning (PPML) has increased. Fully Homomorphic Encryption (FHE) offers a promising solution by enabling computations on encrypted data without exposing the raw data. However, FHE-based neural network inference suffers from substantial overhead due to expensive primitive operations, such as ciphertext rotation and bootstrapping. While previous research has primarily focused on optimizing the efficiency of these computations, our work takes a different approach by concentrating on the rotation keyset design, a pre-generated data structure prepared before execution. We systematically explore three key design spaces (KDS) that influence rotation keyset design and propose an optimized keyset that reduces both computational overhead and memory consumption. To demonstrate the effectiveness of our new KDS design, we present two case studies that achieve up to 11.29× memory reduction and 1.67–2.55× speedup, highlighting the benefits of our optimized keyset.https://www.mdpi.com/1424-8220/25/14/4320machine learning as a serviceprivacy-preserving machine learningneural networksfully homomorphic encryptionCKKSrotation keyset |
| spellingShingle | Youyeon Joo Seungjin Ha Hyunyoung Oh Yunheung Paek Efficient Keyset Design for Neural Networks Using Homomorphic Encryption Sensors machine learning as a service privacy-preserving machine learning neural networks fully homomorphic encryption CKKS rotation keyset |
| title | Efficient Keyset Design for Neural Networks Using Homomorphic Encryption |
| title_full | Efficient Keyset Design for Neural Networks Using Homomorphic Encryption |
| title_fullStr | Efficient Keyset Design for Neural Networks Using Homomorphic Encryption |
| title_full_unstemmed | Efficient Keyset Design for Neural Networks Using Homomorphic Encryption |
| title_short | Efficient Keyset Design for Neural Networks Using Homomorphic Encryption |
| title_sort | efficient keyset design for neural networks using homomorphic encryption |
| topic | machine learning as a service privacy-preserving machine learning neural networks fully homomorphic encryption CKKS rotation keyset |
| url | https://www.mdpi.com/1424-8220/25/14/4320 |
| work_keys_str_mv | AT youyeonjoo efficientkeysetdesignforneuralnetworksusinghomomorphicencryption AT seungjinha efficientkeysetdesignforneuralnetworksusinghomomorphicencryption AT hyunyoungoh efficientkeysetdesignforneuralnetworksusinghomomorphicencryption AT yunheungpaek efficientkeysetdesignforneuralnetworksusinghomomorphicencryption |