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Polynomial Multiplication Systolic Array for Homomorphic Encryption in Secure Network Communications

Sutisna N.a, Jonatan G.a, Syafalni I.a, Mulyawan R.a, Adiono T.a

a University of Excellence Center on Microelectronics, Bandung Institute of Technology, Bandung, Indonesia

[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.Homomorphic encryption is a type of encryption that allows computation to be done directly on encrypted data, without the need to perform any decryption in the process. Brakerski/Fan-Vercauteren (BFV) is a homomorphic encryption scheme that uses Ring Learning with Error (RLWE) problem and encrypts data in ring polynomial form. The encryption and decryption in this scheme involve high degree polynomial multiplication. In this paper, we propose a systolic array design to accelerate polynomial multiplication using convolution method. In implementation result, we show result area for 2×2 and 4×4 polynomial multiplication systolic array, as a proof of concept we show a Verilog simulation for 15 degrees polynomial multiplication, and design space exploration with variation in bit-size and systolic array matrix size. From our exploration we found optimized design size that can be implemented in PYNQ-ZI board. The homomorphic encryption is useful for secure network communications and cloud analytics.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Convolution methods,Design space exploration,Encryption and decryption,Ho-momorphic encryptions,Homomorphic Encryption Schemes,Learning with Errors,Polynomial multiplication,Verilog simulation[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]BFV,homomorphic encryption,polynomial multiplication,secure communications,systolic array[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][{‘$’: ‘The authors gratefully acknowledge support by P3MI ITB and by Indonesian Ministry of Research and Technology / National Agency for Research and Innovation (Kemenristek / BRIN) under National Competition Research grant (Program Penelitian Kompetitif Nasional).’}, {‘$’: ‘ACKNOWLEDGEMENT The authors gratefully acknowledge support by P3MI ITB and by Indonesian Ministry of Research and Technology / National Agency for Research and Innovation (Kemenristek / BRIN) under National Competition Research grant (Program Penelitian Kompetitif Nasional).’}][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/Comnetsat50391.2020.9329002[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]