Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE Article Swipe
Fuli Li
,
Huifang Huang
,
Ting Gao
,
Jin Guo
,
Jinqiao Duan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.09025
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.09025
With the rapid development of AI technology, we have witnessed numerous innovations and conveniences. However, along with these advancements come privacy threats and risks. Fully Homomorphic Encryption (FHE) emerges as a key technology for privacy-preserving computation, enabling computations while maintaining data privacy. Nevertheless, FHE has limitations in processing continuous non-polynomial functions as it is restricted to discrete integers and supports only addition and multiplication. Spiking Neural Networks (SNNs) operate on discrete spike signals, naturally aligning with the properties of FHE. In this paper, we present a framework called FHE-DiCSNN. This framework is based on the efficient TFHE scheme and leverages the discrete properties of SNNs to achieve high prediction performance on ciphertexts. Firstly, by employing bootstrapping techniques, we successfully implement computations of the Leaky Integrate-and-Fire neuron model on ciphertexts. Through bootstrapping, we can facilitate computations for SNNs of arbitrary depth. This framework can be extended to other spiking neuron models, providing a novel framework for the homomorphic evaluation of SNNs. Secondly, inspired by CNNs, we adopt convolutional methods to replace Poisson encoding. This not only enhances accuracy but also mitigates the issue of prolonged simulation time caused by random encoding. Furthermore, we employ engineering techniques to parallelize the computation of bootstrapping, resulting in a significant improvement in computational efficiency. Finally, we evaluate our model on the MNIST dataset. Experimental results demonstrate that, with the optimal parameter configuration, FHE-DiCSNN achieves an accuracy of 97.94% on ciphertexts, with a loss of only 0.53% compared to the original network's accuracy of 98.47%. Moreover, each prediction requires only 0.75 seconds of computation time
Related Topics
Concepts
Homomorphic encryption
Computer science
Bootstrapping (finance)
MNIST database
Computation
Convolutional neural network
Spiking neural network
Encoding (memory)
Encryption
Theoretical computer science
Computer engineering
Artificial intelligence
Algorithm
Artificial neural network
Computer network
Mathematics
Econometrics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.09025
- https://arxiv.org/pdf/2309.09025
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386875238
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4386875238Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2309.09025Digital Object Identifier
- Title
-
Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHEWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-16Full publication date if available
- Authors
-
Fuli Li, Huifang Huang, Ting Gao, Jin Guo, Jinqiao DuanList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.09025Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.09025Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2309.09025Direct OA link when available
- Concepts
-
Homomorphic encryption, Computer science, Bootstrapping (finance), MNIST database, Computation, Convolutional neural network, Spiking neural network, Encoding (memory), Encryption, Theoretical computer science, Computer engineering, Artificial intelligence, Algorithm, Artificial neural network, Computer network, Mathematics, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4386875238 |
|---|---|
| doi | https://doi.org/10.48550/arxiv.2309.09025 |
| ids.doi | https://doi.org/10.48550/arxiv.2309.09025 |
| ids.openalex | https://openalex.org/W4386875238 |
| fwci | |
| type | preprint |
| title | Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10502 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Advanced Memory and Neural Computing |
| topics[1].id | https://openalex.org/T12808 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9919999837875366 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2208 |
| topics[1].subfield.display_name | Electrical and Electronic Engineering |
| topics[1].display_name | Ferroelectric and Negative Capacitance Devices |
| topics[2].id | https://openalex.org/T11612 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.973800003528595 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Stochastic Gradient Optimization Techniques |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C158338273 |
| concepts[0].level | 3 |
| concepts[0].score | 0.8582242727279663 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2154943 |
| concepts[0].display_name | Homomorphic encryption |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.8275513648986816 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C207609745 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7859033942222595 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q4944086 |
| concepts[2].display_name | Bootstrapping (finance) |
| concepts[3].id | https://openalex.org/C190502265 |
| concepts[3].level | 3 |
| concepts[3].score | 0.7535991072654724 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q17069496 |
| concepts[3].display_name | MNIST database |
| concepts[4].id | https://openalex.org/C45374587 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5647008419036865 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12525525 |
| concepts[4].display_name | Computation |
| concepts[5].id | https://openalex.org/C81363708 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5612563490867615 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[5].display_name | Convolutional neural network |
| concepts[6].id | https://openalex.org/C11731999 |
| concepts[6].level | 3 |
| concepts[6].score | 0.5356485247612 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q9067355 |
| concepts[6].display_name | Spiking neural network |
| concepts[7].id | https://openalex.org/C125411270 |
| concepts[7].level | 2 |
| concepts[7].score | 0.45241403579711914 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q18653 |
| concepts[7].display_name | Encoding (memory) |
| concepts[8].id | https://openalex.org/C148730421 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4456263780593872 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q141090 |
| concepts[8].display_name | Encryption |
| concepts[9].id | https://openalex.org/C80444323 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4330863356590271 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[9].display_name | Theoretical computer science |
| concepts[10].id | https://openalex.org/C113775141 |
| concepts[10].level | 1 |
| concepts[10].score | 0.38944336771965027 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q428691 |
| concepts[10].display_name | Computer engineering |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3814897835254669 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C11413529 |
| concepts[12].level | 1 |
| concepts[12].score | 0.32682323455810547 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[12].display_name | Algorithm |
| concepts[13].id | https://openalex.org/C50644808 |
| concepts[13].level | 2 |
| concepts[13].score | 0.306041955947876 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[13].display_name | Artificial neural network |
| concepts[14].id | https://openalex.org/C31258907 |
| concepts[14].level | 1 |
| concepts[14].score | 0.08332309126853943 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[14].display_name | Computer network |
| concepts[15].id | https://openalex.org/C33923547 |
| concepts[15].level | 0 |
| concepts[15].score | 0.08199727535247803 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[15].display_name | Mathematics |
| concepts[16].id | https://openalex.org/C149782125 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[16].display_name | Econometrics |
| keywords[0].id | https://openalex.org/keywords/homomorphic-encryption |
| keywords[0].score | 0.8582242727279663 |
| keywords[0].display_name | Homomorphic encryption |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.8275513648986816 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/bootstrapping |
| keywords[2].score | 0.7859033942222595 |
| keywords[2].display_name | Bootstrapping (finance) |
| keywords[3].id | https://openalex.org/keywords/mnist-database |
| keywords[3].score | 0.7535991072654724 |
| keywords[3].display_name | MNIST database |
| keywords[4].id | https://openalex.org/keywords/computation |
| keywords[4].score | 0.5647008419036865 |
| keywords[4].display_name | Computation |
| keywords[5].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[5].score | 0.5612563490867615 |
| keywords[5].display_name | Convolutional neural network |
| keywords[6].id | https://openalex.org/keywords/spiking-neural-network |
| keywords[6].score | 0.5356485247612 |
| keywords[6].display_name | Spiking neural network |
| keywords[7].id | https://openalex.org/keywords/encoding |
| keywords[7].score | 0.45241403579711914 |
| keywords[7].display_name | Encoding (memory) |
| keywords[8].id | https://openalex.org/keywords/encryption |
| keywords[8].score | 0.4456263780593872 |
| keywords[8].display_name | Encryption |
| keywords[9].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[9].score | 0.4330863356590271 |
| keywords[9].display_name | Theoretical computer science |
| keywords[10].id | https://openalex.org/keywords/computer-engineering |
| keywords[10].score | 0.38944336771965027 |
| keywords[10].display_name | Computer engineering |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.3814897835254669 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/algorithm |
| keywords[12].score | 0.32682323455810547 |
| keywords[12].display_name | Algorithm |
| keywords[13].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[13].score | 0.306041955947876 |
| keywords[13].display_name | Artificial neural network |
| keywords[14].id | https://openalex.org/keywords/computer-network |
| keywords[14].score | 0.08332309126853943 |
| keywords[14].display_name | Computer network |
| keywords[15].id | https://openalex.org/keywords/mathematics |
| keywords[15].score | 0.08199727535247803 |
| keywords[15].display_name | Mathematics |
| language | en |
| locations[0].id | pmh:oai:arXiv.org:2309.09025 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400194 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | arXiv (Cornell University) |
| locations[0].source.host_organization | https://openalex.org/I205783295 |
| locations[0].source.host_organization_name | Cornell University |
| locations[0].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[0].license | |
| locations[0].pdf_url | https://arxiv.org/pdf/2309.09025 |
| locations[0].version | submittedVersion |
| locations[0].raw_type | text |
| locations[0].license_id | |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | http://arxiv.org/abs/2309.09025 |
| locations[1].id | doi:10.48550/arxiv.2309.09025 |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400194 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | arXiv (Cornell University) |
| locations[1].source.host_organization | https://openalex.org/I205783295 |
| locations[1].source.host_organization_name | Cornell University |
| locations[1].source.host_organization_lineage | https://openalex.org/I205783295 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | |
| locations[1].raw_source_name | |
| locations[1].landing_page_url | https://doi.org/10.48550/arxiv.2309.09025 |
| indexed_in | arxiv, datacite |
| authorships[0].author.id | https://openalex.org/A5108059444 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3611-2886 |
| authorships[0].author.display_name | Fuli Li |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Li, Pengbo |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5083371329 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4901-8928 |
| authorships[1].author.display_name | Huifang Huang |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Huang, Huifang |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5100680092 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-7840-7060 |
| authorships[2].author.display_name | Ting Gao |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Gao, Ting |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5100576554 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Jin Guo |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Guo, Jin |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5034610999 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-2077-990X |
| authorships[4].author.display_name | Jinqiao Duan |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Duan, Jinqiao |
| authorships[4].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://arxiv.org/pdf/2309.09025 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-09-20T00:00:00 |
| display_name | Efficient Privacy-Preserving Convolutional Spiking Neural Networks with FHE |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T06:51:31.235846 |
| primary_topic.id | https://openalex.org/T10502 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Advanced Memory and Neural Computing |
| related_works | https://openalex.org/W4281699635, https://openalex.org/W4321472116, https://openalex.org/W2152926062, https://openalex.org/W3202619090, https://openalex.org/W2949607150, https://openalex.org/W3102040318, https://openalex.org/W2950312267, https://openalex.org/W4287724471, https://openalex.org/W3214713078, https://openalex.org/W2786930404 |
| cited_by_count | 0 |
| locations_count | 2 |
| best_oa_location.id | pmh:oai:arXiv.org:2309.09025 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400194 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | arXiv (Cornell University) |
| best_oa_location.source.host_organization | https://openalex.org/I205783295 |
| best_oa_location.source.host_organization_name | Cornell University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://arxiv.org/pdf/2309.09025 |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | text |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | http://arxiv.org/abs/2309.09025 |
| primary_location.id | pmh:oai:arXiv.org:2309.09025 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400194 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | arXiv (Cornell University) |
| primary_location.source.host_organization | https://openalex.org/I205783295 |
| primary_location.source.host_organization_name | Cornell University |
| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2309.09025 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | text |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2309.09025 |
| publication_date | 2023-09-16 |
| publication_year | 2023 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 30, 85, 151, 203, 236 |
| abstract_inverted_index.AI | 5 |
| abstract_inverted_index.In | 80 |
| abstract_inverted_index.an | 229 |
| abstract_inverted_index.as | 29, 51 |
| abstract_inverted_index.be | 143 |
| abstract_inverted_index.by | 113, 162, 187 |
| abstract_inverted_index.in | 46, 202, 206 |
| abstract_inverted_index.is | 53, 91 |
| abstract_inverted_index.it | 52 |
| abstract_inverted_index.of | 4, 78, 103, 121, 137, 158, 182, 199, 231, 238, 247, 256 |
| abstract_inverted_index.on | 69, 93, 110, 127, 214, 233 |
| abstract_inverted_index.to | 55, 105, 145, 168, 195, 242 |
| abstract_inverted_index.we | 7, 83, 117, 131, 164, 191, 210 |
| abstract_inverted_index.FHE | 43 |
| abstract_inverted_index.and | 12, 22, 58, 62, 98 |
| abstract_inverted_index.but | 177 |
| abstract_inverted_index.can | 132, 142 |
| abstract_inverted_index.for | 33, 135, 154 |
| abstract_inverted_index.has | 44 |
| abstract_inverted_index.key | 31 |
| abstract_inverted_index.not | 173 |
| abstract_inverted_index.our | 212 |
| abstract_inverted_index.the | 1, 76, 94, 100, 122, 155, 180, 197, 215, 223, 243 |
| abstract_inverted_index.0.75 | 254 |
| abstract_inverted_index.FHE. | 79 |
| abstract_inverted_index.SNNs | 104, 136 |
| abstract_inverted_index.TFHE | 96 |
| abstract_inverted_index.This | 89, 140, 172 |
| abstract_inverted_index.With | 0 |
| abstract_inverted_index.also | 178 |
| abstract_inverted_index.come | 19 |
| abstract_inverted_index.data | 40 |
| abstract_inverted_index.each | 250 |
| abstract_inverted_index.have | 8 |
| abstract_inverted_index.high | 107 |
| abstract_inverted_index.loss | 237 |
| abstract_inverted_index.only | 60, 174, 239, 253 |
| abstract_inverted_index.this | 81 |
| abstract_inverted_index.time | 185, 258 |
| abstract_inverted_index.with | 16, 75, 222, 235 |
| abstract_inverted_index.(FHE) | 27 |
| abstract_inverted_index.0.53% | 240 |
| abstract_inverted_index.CNNs, | 163 |
| abstract_inverted_index.Fully | 24 |
| abstract_inverted_index.Leaky | 123 |
| abstract_inverted_index.MNIST | 216 |
| abstract_inverted_index.SNNs. | 159 |
| abstract_inverted_index.adopt | 165 |
| abstract_inverted_index.along | 15 |
| abstract_inverted_index.based | 92 |
| abstract_inverted_index.issue | 181 |
| abstract_inverted_index.model | 126, 213 |
| abstract_inverted_index.novel | 152 |
| abstract_inverted_index.other | 146 |
| abstract_inverted_index.rapid | 2 |
| abstract_inverted_index.spike | 71 |
| abstract_inverted_index.that, | 221 |
| abstract_inverted_index.these | 17 |
| abstract_inverted_index.while | 38 |
| abstract_inverted_index.(SNNs) | 67 |
| abstract_inverted_index.97.94% | 232 |
| abstract_inverted_index.Neural | 65 |
| abstract_inverted_index.called | 87 |
| abstract_inverted_index.caused | 186 |
| abstract_inverted_index.depth. | 139 |
| abstract_inverted_index.employ | 192 |
| abstract_inverted_index.neuron | 125, 148 |
| abstract_inverted_index.paper, | 82 |
| abstract_inverted_index.random | 188 |
| abstract_inverted_index.risks. | 23 |
| abstract_inverted_index.scheme | 97 |
| abstract_inverted_index.98.47%. | 248 |
| abstract_inverted_index.Poisson | 170 |
| abstract_inverted_index.Spiking | 64 |
| abstract_inverted_index.Through | 129 |
| abstract_inverted_index.achieve | 106 |
| abstract_inverted_index.emerges | 28 |
| abstract_inverted_index.methods | 167 |
| abstract_inverted_index.models, | 149 |
| abstract_inverted_index.operate | 68 |
| abstract_inverted_index.optimal | 224 |
| abstract_inverted_index.present | 84 |
| abstract_inverted_index.privacy | 20 |
| abstract_inverted_index.replace | 169 |
| abstract_inverted_index.results | 219 |
| abstract_inverted_index.seconds | 255 |
| abstract_inverted_index.spiking | 147 |
| abstract_inverted_index.threats | 21 |
| abstract_inverted_index.Finally, | 209 |
| abstract_inverted_index.Firstly, | 112 |
| abstract_inverted_index.However, | 14 |
| abstract_inverted_index.Networks | 66 |
| abstract_inverted_index.accuracy | 176, 230, 246 |
| abstract_inverted_index.achieves | 228 |
| abstract_inverted_index.addition | 61 |
| abstract_inverted_index.aligning | 74 |
| abstract_inverted_index.compared | 241 |
| abstract_inverted_index.dataset. | 217 |
| abstract_inverted_index.discrete | 56, 70, 101 |
| abstract_inverted_index.enabling | 36 |
| abstract_inverted_index.enhances | 175 |
| abstract_inverted_index.evaluate | 211 |
| abstract_inverted_index.extended | 144 |
| abstract_inverted_index.inspired | 161 |
| abstract_inverted_index.integers | 57 |
| abstract_inverted_index.numerous | 10 |
| abstract_inverted_index.original | 244 |
| abstract_inverted_index.privacy. | 41 |
| abstract_inverted_index.requires | 252 |
| abstract_inverted_index.signals, | 72 |
| abstract_inverted_index.supports | 59 |
| abstract_inverted_index.Moreover, | 249 |
| abstract_inverted_index.Secondly, | 160 |
| abstract_inverted_index.arbitrary | 138 |
| abstract_inverted_index.efficient | 95 |
| abstract_inverted_index.employing | 114 |
| abstract_inverted_index.encoding. | 171, 189 |
| abstract_inverted_index.framework | 86, 90, 141, 153 |
| abstract_inverted_index.functions | 50 |
| abstract_inverted_index.implement | 119 |
| abstract_inverted_index.leverages | 99 |
| abstract_inverted_index.mitigates | 179 |
| abstract_inverted_index.naturally | 73 |
| abstract_inverted_index.network's | 245 |
| abstract_inverted_index.parameter | 225 |
| abstract_inverted_index.prolonged | 183 |
| abstract_inverted_index.providing | 150 |
| abstract_inverted_index.resulting | 201 |
| abstract_inverted_index.witnessed | 9 |
| abstract_inverted_index.Encryption | 26 |
| abstract_inverted_index.FHE-DiCSNN | 227 |
| abstract_inverted_index.continuous | 48 |
| abstract_inverted_index.evaluation | 157 |
| abstract_inverted_index.facilitate | 133 |
| abstract_inverted_index.prediction | 108, 251 |
| abstract_inverted_index.processing | 47 |
| abstract_inverted_index.properties | 77, 102 |
| abstract_inverted_index.restricted | 54 |
| abstract_inverted_index.simulation | 184 |
| abstract_inverted_index.techniques | 194 |
| abstract_inverted_index.technology | 32 |
| abstract_inverted_index.FHE-DiCSNN. | 88 |
| abstract_inverted_index.Homomorphic | 25 |
| abstract_inverted_index.computation | 198, 257 |
| abstract_inverted_index.demonstrate | 220 |
| abstract_inverted_index.development | 3 |
| abstract_inverted_index.efficiency. | 208 |
| abstract_inverted_index.engineering | 193 |
| abstract_inverted_index.homomorphic | 156 |
| abstract_inverted_index.improvement | 205 |
| abstract_inverted_index.innovations | 11 |
| abstract_inverted_index.limitations | 45 |
| abstract_inverted_index.maintaining | 39 |
| abstract_inverted_index.parallelize | 196 |
| abstract_inverted_index.performance | 109 |
| abstract_inverted_index.significant | 204 |
| abstract_inverted_index.techniques, | 116 |
| abstract_inverted_index.technology, | 6 |
| abstract_inverted_index.Experimental | 218 |
| abstract_inverted_index.Furthermore, | 190 |
| abstract_inverted_index.advancements | 18 |
| abstract_inverted_index.ciphertexts, | 234 |
| abstract_inverted_index.ciphertexts. | 111, 128 |
| abstract_inverted_index.computation, | 35 |
| abstract_inverted_index.computations | 37, 120, 134 |
| abstract_inverted_index.successfully | 118 |
| abstract_inverted_index.Nevertheless, | 42 |
| abstract_inverted_index.bootstrapping | 115 |
| abstract_inverted_index.computational | 207 |
| abstract_inverted_index.conveniences. | 13 |
| abstract_inverted_index.convolutional | 166 |
| abstract_inverted_index.bootstrapping, | 130, 200 |
| abstract_inverted_index.configuration, | 226 |
| abstract_inverted_index.non-polynomial | 49 |
| abstract_inverted_index.multiplication. | 63 |
| abstract_inverted_index.Integrate-and-Fire | 124 |
| abstract_inverted_index.privacy-preserving | 34 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |