Difference between revisions of "Rouhani2018redcrypt"
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|url=https://dl.acm.org/citation.cfm?id=3242899 | |url=https://dl.acm.org/citation.cfm?id=3242899 | ||
|abstract=<p><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">Artificial Intelligence (AI) is increasingly incorporated into the\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">cloud business</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ in order to improve the functionality (e.g., accuracy) of the service. The adoption of AI as a cloud service raises serious privacy concerns in applications where the\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">risk of data leakage</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ is not acceptable. Examples of such applications include scenarios where clients hold potentially sensitive private information such as medical records, financial data, and/or location. This article proposes ReDCrypt, the first\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">reconfigurable</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ hardware-accelerated framework that empowers privacy-preserving inference of deep learning models in cloud servers. ReDCrypt is well-suited for\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">streaming (a.k.a., real-time AI)</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ settings where clients need to dynamically analyze their data as it is collected over time without having to queue the samples to meet a certain batch size. Unlike prior work, ReDCrypt neither requires to change how AI models are trained nor relies on two non-colluding servers to perform. The privacy-preserving computation in ReDCrypt is executed using\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">Yao\’s Garbled Circuit</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ (GC) protocol. We break down the deep learning inference task into two phases: (i) privacy-insensitive (local) computation, and (ii) privacy-sensitive (interactive) computation. We devise a high-throughput and power-efficient implementation of GC protocol on FPGA for the privacy-sensitive phase. ReDCrypt\’s accompanying API provides support for seamless integration of ReDCrypt into any deep learning framework. Proof-of-concept evaluations for different DL applications demonstrate up to 57-fold higher throughput per core compared to the best prior solution with no drop in the accuracy.</span></p> | |abstract=<p><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">Artificial Intelligence (AI) is increasingly incorporated into the\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">cloud business</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ in order to improve the functionality (e.g., accuracy) of the service. The adoption of AI as a cloud service raises serious privacy concerns in applications where the\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">risk of data leakage</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ is not acceptable. Examples of such applications include scenarios where clients hold potentially sensitive private information such as medical records, financial data, and/or location. This article proposes ReDCrypt, the first\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">reconfigurable</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ hardware-accelerated framework that empowers privacy-preserving inference of deep learning models in cloud servers. ReDCrypt is well-suited for\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">streaming (a.k.a., real-time AI)</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ settings where clients need to dynamically analyze their data as it is collected over time without having to queue the samples to meet a certain batch size. Unlike prior work, ReDCrypt neither requires to change how AI models are trained nor relies on two non-colluding servers to perform. The privacy-preserving computation in ReDCrypt is executed using\ </span><i style="box-sizing: border-box; color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">Yao\’s Garbled Circuit</i><span style="color: rgb(0, 0, 0); font-family: Verdana, Arial, Helvetica, sans-serif;">\ (GC) protocol. We break down the deep learning inference task into two phases: (i) privacy-insensitive (local) computation, and (ii) privacy-sensitive (interactive) computation. We devise a high-throughput and power-efficient implementation of GC protocol on FPGA for the privacy-sensitive phase. ReDCrypt\’s accompanying API provides support for seamless integration of ReDCrypt into any deep learning framework. Proof-of-concept evaluations for different DL applications demonstrate up to 57-fold higher throughput per core compared to the best prior solution with no drop in the accuracy.</span></p> | ||
|journal=ACM Transactions on Reconfigurable Technology and Systems (TRETS) - Special Issue on Deep learning on FPGAs | |month=12 | ||
|year=2018 | |||
|journal=ACM Transactions on Reconfigurable Technology and Systems (TRETS) - Special Issue on Deep learning on FPGAs | |||
|title=ReDCrypt: Real-Time Privacy-Preserving Deep Learning Inference in Clouds Using FPGAs | |title=ReDCrypt: Real-Time Privacy-Preserving Deep Learning Inference in Clouds Using FPGAs | ||
|entry=article | |entry=article | ||
| | |pdf=Rouhani2018redcrypt.pdf | ||
}} | }} | ||
Latest revision as of 17:39, 9 November 2021
| Rouhani2018redcrypt | |
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| entry | article |
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| author | Rouhani, Bita D and Siam U. Hussain and Kristin Lauter and Farinaz Koushanfar |
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| journal | ACM Transactions on Reconfigurable Technology and Systems (TRETS) - Special Issue on Deep learning on FPGAs |
| month | 12 |
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| title | ReDCrypt: Real-Time Privacy-Preserving Deep Learning Inference in Clouds Using FPGAs |
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| year | 2018 |
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| url | https://dl.acm.org/citation.cfm?id=3242899 |
| Rouhani2018redcrypt.pdf | |
Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093