{"id":"https://openalex.org/W7170053041","doi":"https://doi.org/10.48550/arxiv.2607.18342","title":"PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption","display_name":"PRISM: Sensitivity-Aware PolynoMial PRuning for EffIcient Neural Network Encryption","publication_year":2026,"publication_date":"2026-07-20","ids":{"openalex":"https://openalex.org/W7170053041","doi":"https://doi.org/10.48550/arxiv.2607.18342"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2607.18342","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.18342","pdf_url":"https://arxiv.org/pdf/2607.18342","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2607.18342","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5143454914","display_name":"Sahaj Majavdia","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Majavdia, Sahaj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5122884543","display_name":"Mahdi Taheri","orcid":"https://orcid.org/0009-0002-0373-5232","observed_orcids":["https://orcid.org/0009-0002-0373-5232"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Taheri, Mahdi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9042999744415283,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.9042999744415283,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10951","display_name":"Cryptographic Implementations and Security","score":0.0142000000923872,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11424","display_name":"Security and Verification in Computing","score":0.010099999606609344,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/neural-network-encryption","display_name":"neural network encryption","score":0.8399999737739563},{"id":"https://openalex.org/keywords/homomorphic-encryption","display_name":"homomorphic encryption","score":0.8949999809265137},{"id":"https://openalex.org/keywords/structured-pruning","display_name":"structured pruning","score":0.8539999723434448},{"id":"https://openalex.org/keywords/ckks","display_name":"CKKS scheme","score":0.7080000042915344},{"id":"https://openalex.org/keywords/fault-injection","display_name":"fault injection","score":0.5920000076293945},{"id":"https://openalex.org/keywords/bit-flip-attacks","display_name":"bit-flip attacks","score":0.6819999814033508},{"id":"https://openalex.org/keywords/model-reliability","display_name":"model reliability","score":0.492000013589859},{"id":"https://openalex.org/keywords/polynomial-neural-network","display_name":"polynomial neural network","score":0.33000001311302185}],"concepts":[{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.6650000214576721},{"id":"https://openalex.org/C158338273","wikidata":"https://www.wikidata.org/wiki/Q2154943","display_name":"Homomorphic encryption","level":3,"score":0.6157000064849854},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6114000082015991},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5914999842643738},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4851999878883362},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48399999737739563},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.48010000586509705},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.412200003862381},{"id":"https://openalex.org/C56683213","wikidata":"https://www.wikidata.org/wiki/Q4143008","display_name":"Homomorphic filtering","level":4,"score":0.38760000467300415},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.35589998960494995},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.35429999232292175},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3434999883174896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.33570000529289246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32839998602867126},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.304500013589859},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2865000069141388},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C63540848","wikidata":"https://www.wikidata.org/wiki/Q3140932","display_name":"Fault tolerance","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2607.18342","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.18342","pdf_url":"https://arxiv.org/pdf/2607.18342","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2607.18342","doi":"https://doi.org/10.48550/arxiv.2607.18342","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.18342","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2607.18342","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2607.18342","pdf_url":"https://arxiv.org/pdf/2607.18342","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G8689881305","display_name":"CRASHLESS - Cross-Layer Reliability and Self-Health Awareness for Intelligent Autonomous Systems","funder_award_id":"PRG1467","funder_id":"https://openalex.org/F4320321090","funder_display_name":"Eesti Teadusagentuur"},{"id":"https://openalex.org/G900587751","display_name":null,"funder_award_id":"PUT PRG1467","funder_id":"https://openalex.org/F4320321090","funder_display_name":"Eesti Teadusagentuur"}],"funders":[{"id":"https://openalex.org/F4320321090","display_name":"Eesti Teadusagentuur","ror":"https://ror.org/00jjeja18"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7170053041.pdf","grobid_xml":"https://content.openalex.org/works/W7170053041.grobid-xml"},"referenced_works_count":3,"referenced_works":["https://openalex.org/W4226460675","https://openalex.org/W4414788696","https://openalex.org/W7140329937"],"related_works":["https://openalex.org/W7163586139","https://openalex.org/W4297030362","https://openalex.org/W4390698576","https://openalex.org/W4406079027","https://openalex.org/W3214802126","https://openalex.org/W7160271646","https://openalex.org/W7203778586","https://openalex.org/W4308641907","https://openalex.org/W7127595666","https://openalex.org/W4411996890"],"abstract_inverted_index":{"Structured":[0],"pruning":[1,42,63],"is":[2,45],"essential":[3],"for":[4,100,141],"making":[5],"neural":[6,33],"network":[7],"inference":[8,186],"feasible":[9],"under":[10,111],"homomorphic":[11],"encryption":[12],"(HE),":[13],"yet":[14],"its":[15],"impact":[16],"on":[17,168],"model":[18],"reliability":[19,28,134,153],"has":[20],"remained":[21],"unexplored.":[22],"This":[23],"paper":[24],"presents":[25],"a":[26,40,121,132],"systematic":[27],"characterization":[29],"of":[30,144],"pruned":[31],"CKKS-encrypted":[32],"networks":[34],"and":[35,58,75,81,170],"introduces":[36],"Polynomial-Sensitivity-Aware":[37],"Pruning":[38],"(PSAP),":[39],"structured":[41],"method":[43],"that":[44],"inherently":[46],"reliability-aware.":[47],"PSAP":[48,160],"scores":[49],"filters":[50],"jointly":[51],"by":[52,106,164],"weight":[53],"magnitude,":[54],"polynomial":[55],"activation":[56],"sensitivity,":[57],"rotation":[59],"cost,":[60],"which":[61],"concentrates":[62],"in":[64],"fault-tolerant":[65],"regions.":[66],"Across":[67],"two":[68,70,72,97],"architectures,":[69],"datasets,":[71],"numerical":[73],"representations,":[74],"five":[76],"bit-error":[77],"rates":[78],"(40":[79],"full-model":[80],"108":[82],"per-layer":[83],"experiments),":[84],"PSAP-pruned":[85],"models":[86],"limit":[87],"catastrophic":[88],"(>10":[89],"pp":[90],"accuracy":[91],"drop)":[92],"layers":[93,139],"to":[94,108,166,181],"at":[95,149],"most":[96],"versus":[98],"5--14":[99],"magnitude-pruned":[101],"baselines,":[102],"reducing":[103],"worst-case":[104],"vulnerability":[105],"up":[107,165],"29":[109],"times":[110],"int32":[112,129],"bit-flip":[113],"injection.":[114],"Direct":[115],"CKKS":[116],"encrypted":[117],"fault":[118],"injection":[119,130],"indicates":[120],"safe":[122],"operating":[123],"boundary":[124],"near":[125],"BER~":[126],"10^{-5},":[127],"supporting":[128],"as":[131],"conservative":[133],"proxy.":[135],"The":[136],"fault-critical":[137],"structural":[138],"account":[140],"only":[142],"1.1%":[143],"parameters,":[145],"enabling":[146,184],"selective":[147],"hardening":[148],"minimal":[150],"overhead.":[151],"These":[152],"gains":[154],"are":[155],"obtained":[156],"alongside":[157],"competitive":[158],"efficiency:":[159],"reduces":[161],"Halevi--Shoup":[162],"rotations":[163],"45.2\\%":[167],"ResNet-32,":[169],"an":[171],"adaptive":[172],"mixed-degree":[173],"allocation":[174],"scheme":[175],"lowers":[176],"multiplicative":[177],"depth":[178],"from":[179],"66":[180],"56":[182],"levels,":[183],"leveled":[185],"without":[187],"bootstrapping.":[188]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-07-23T00:00:00"}