Difference between revisions of "Songhori2015"
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|keywords=Data Mining, Garbled Circuit, k-NN, Logic Design, Logic Synthesis, Nearest Neighbor, Privacy-Preserving, Secure Function Evaluation | |keywords=Data Mining, Garbled Circuit, k-NN, Logic Design, Logic Synthesis, Nearest Neighbor, Privacy-Preserving, Secure Function Evaluation | ||
|abstract=<p>This paper introduces the first efficient, scalable, and practical method for privacy-preserving k-nearest neighbors (kNN) search. The approach enables performing the widely used k-NN search in sensitive scenarios where none of the parties reveal their information while they can still cooperatively find the nearest matches. The privacy preservation is based on the Yao\’s garbled circuit (GC) protocol. In contrast with the existing GC approaches that only accept function descriptions as combinational circuits, we suggest using sequential circuits. This work introduces novel transformations, such that the sequential description can be evaluated by interfacing with the existing GC schemes that only accept combinational circuits. We demonstrate a great effi- ciency in the memory required for realizing the secure k-NN search. The first-of-a-kind implementation of privacy preserving k-NN, utilizing the Synopsys Design Compiler on a conventional Intel processor demonstrates the applicability, efficiency, and scalability of the suggested methods.</p> | |abstract=<p>This paper introduces the first efficient, scalable, and practical method for privacy-preserving k-nearest neighbors (kNN) search. The approach enables performing the widely used k-NN search in sensitive scenarios where none of the parties reveal their information while they can still cooperatively find the nearest matches. The privacy preservation is based on the Yao\’s garbled circuit (GC) protocol. In contrast with the existing GC approaches that only accept function descriptions as combinational circuits, we suggest using sequential circuits. This work introduces novel transformations, such that the sequential description can be evaluated by interfacing with the existing GC schemes that only accept combinational circuits. We demonstrate a great effi- ciency in the memory required for realizing the secure k-NN search. The first-of-a-kind implementation of privacy preserving k-NN, utilizing the Synopsys Design Compiler on a conventional Intel processor demonstrates the applicability, efficiency, and scalability of the suggested methods.</p> | ||
|month=6 | |||
|year=2015 | |||
|booktitle=Design Automation Conference (DAC) | |booktitle=Design Automation Conference (DAC) | ||
|title=Compacting privacy-preserving k-nearest neighbor search using logic synthesis | |title=Compacting privacy-preserving k-nearest neighbor search using logic synthesis | ||
|entry=inproceedings | |entry=inproceedings | ||
}} | }} | ||
Revision as of 03:42, 4 September 2021
| Songhori2015 | |
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| entry | inproceedings |
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| author | E. Songhori and Siam U. Hussain and Ahmad-Reza Sadeghi and F. Koushanfar |
| booktitle | Design Automation Conference (DAC) |
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| journal | |
| month | 6 |
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| title | Compacting privacy-preserving k-nearest neighbor search using logic synthesis |
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| year | 2015 |
| doi | 10.1145/2744769.2744808 |
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| url | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7167220\&tag=1 |
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