Deep Learning Inferences with Hybrid Homomorphic Encryption
When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge, there has been no complete implementation of common deep learning operations, for arbitrary model depths, using homomorphic encryption. This paper demonstrates a novel approach, efficiently implementing many deep learning functions with bootstrapped homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.
 This paper proposes a hybrid Homomorphic encryption system that is well suited for privacy-sensitive data inference applications with the deep learning paradigm. The paper presents a well laid research methodology that shows a good decomposition of the problem at hand and the approach foreseen to solve it. It is well reflected in the paper and most importantly the rationale for the implementation decisions taken is always clear. The results obtained (as compared to FHEW) seem to indicate well thought off decisions taken to optimize the different gates* operations as clearly explained in the paper. For example, reducing bootstrapping operations by two-complementing both the plaintext and the ciphertext, whenever the number of 1s in the plain bit-string is greater than the number of 0s (3.4/Page 6). Result interpretation is coherent with the approach and data used and shows a good understanding of the implications of the implementation decisions made in the system and the data sets used. Overall, fine work, well organized, decomposed, and its rationale clearly explained. The good results obtained support the design decisions made. Our main concern is regarding thorough comparison to similar work and provision of comparative work assessment to support novelty claims. Nota: - In Figure 4/Page 4: AND Table A(1)/B(0), shouldn*t A And B be 0? - Unlike Figure 3/Page 3, in Figure 2/page 2, shouldn*t operations* precedence prevail (No brackets), therefore 1+2*2=5?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Deep Learning Inferences with Hybrid Homomorphic Encryption
When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge, there has been no complete implementation of common deep learning operations, for arbitrary model depths, using homomorphic encryption. This paper demonstrates a novel approach, efficiently implementing many deep learning functions with bootstrapped homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper proposes a hybrid Homomorphic encryption system that is well suited for privacy-sensitive data inference applications with the deep learning paradigm. The paper presents a well laid research methodology that shows a good decomposition of the problem at hand and the approach foreseen to solve it. It is well reflected in the paper and most importantly the rationale for the implementation decisions taken is always clear. The results obtained (as compared to FHEW) seem to indicate well thought off decisions taken to optimize the different gates* operations as clearly explained in the paper. For example, reducing bootstrapping operations by two-complementing both the plaintext and the ciphertext, whenever the number of 1s in the plain bit-string is greater than the number of 0s (3.4/Page 6). Result interpretation is coherent with the approach and data used and shows a good understanding of the implications of the implementation decisions made in the system and the data sets used. Overall, fine work, well organized, decomposed, and its rationale clearly explained. The good results obtained support the design decisions made. Our main concern is regarding thorough comparison to similar work and provision of comparative work assessment to support novelty claims. Nota: - In Figure 4/Page 4: AND Table A(1)/B(0), shouldn*t A And B be 0? - Unlike Figure 3/Page 3, in Figure 2/page 2, shouldn*t operations* precedence prevail (No brackets), therefore 1+2*2=5?
assistant
 This paper proposes a hybrid Homomorphic encryption system that is well suited for privacy-sensitive data inference applications with the deep learning paradigm. The paper presents a well laid research methodology that shows a good decomposition of the problem at hand and the approach foreseen to solve it. It is well reflected in the paper and most importantly the rationale for the implementation decisions taken is always clear. The results obtained (as compared to FHEW) seem to indicate well thought off decisions taken to optimize the different gates* operations as clearly explained in the paper. For example, reducing bootstrapping operations by two-complementing both the plaintext and the ciphertext, whenever the number of 1s in the plain bit-string is greater than the number of 0s (3.4/Page 6). Result interpretation is coherent with the approach and data used and shows a good understanding of the implications of the implementation decisions made in the system and the data sets used. Overall, fine work, well organized, decomposed, and its rationale clearly explained. The good results obtained support the design decisions made. Our main concern is regarding thorough comparison to similar work and provision of comparative work assessment to support novelty claims. Nota: - In Figure 4/Page 4: AND Table A(1)/B(0), shouldn*t A And B be 0? - Unlike Figure 3/Page 3, in Figure 2/page 2, shouldn*t operations* precedence prevail (No brackets), therefore 1+2*2=5?