{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TSA2JNH2KP43MBGUOLNMFZVGQX","short_pith_number":"pith:TSA2JNH2","schema_version":"1.0","canonical_sha256":"9c81a4b4fa53f9b604d472dac2e6a685f04ed831daea8c675d27806578af2e30","source":{"kind":"arxiv","id":"1901.09706","version":1},"attestation_state":"computed","paper":{"title":"Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Fu Song, Hongyi Xie, Jun Zhang, Pengfei Gao, Taolue Chen","submitted_at":"2019-01-28T14:50:38Z","abstract_excerpt":"Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for com"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1901.09706","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2019-01-28T14:50:38Z","cross_cats_sorted":[],"title_canon_sha256":"d7cdfac2ac291fe07907a18e633f4d7fbbe5e785ad6a9ad4e7af2e69a5d360dd","abstract_canon_sha256":"34e34ee2622b2a258c9db428dace38e0a1323ea4d91a88109fb8c783925f4f40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:55:24.033851Z","signature_b64":"jzJps+dHqZscp8XBxSJbRRcZ0qrpnme9u7sobzejI8bdfAV8YLAJR/RQo3NKm5j1/fm064LXgJdLE515nFZhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c81a4b4fa53f9b604d472dac2e6a685f04ed831daea8c675d27806578af2e30","last_reissued_at":"2026-05-17T23:55:24.033264Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:55:24.033264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Fu Song, Hongyi Xie, Jun Zhang, Pengfei Gao, Taolue Chen","submitted_at":"2019-01-28T14:50:38Z","abstract_excerpt":"Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.09706","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1901.09706","created_at":"2026-05-17T23:55:24.033367+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.09706v1","created_at":"2026-05-17T23:55:24.033367+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.09706","created_at":"2026-05-17T23:55:24.033367+00:00"},{"alias_kind":"pith_short_12","alias_value":"TSA2JNH2KP43","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"TSA2JNH2KP43MBGU","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"TSA2JNH2","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX","json":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX.json","graph_json":"https://pith.science/api/pith-number/TSA2JNH2KP43MBGUOLNMFZVGQX/graph.json","events_json":"https://pith.science/api/pith-number/TSA2JNH2KP43MBGUOLNMFZVGQX/events.json","paper":"https://pith.science/paper/TSA2JNH2"},"agent_actions":{"view_html":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX","download_json":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX.json","view_paper":"https://pith.science/paper/TSA2JNH2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.09706&json=true","fetch_graph":"https://pith.science/api/pith-number/TSA2JNH2KP43MBGUOLNMFZVGQX/graph.json","fetch_events":"https://pith.science/api/pith-number/TSA2JNH2KP43MBGUOLNMFZVGQX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX/action/storage_attestation","attest_author":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX/action/author_attestation","sign_citation":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX/action/citation_signature","submit_replication":"https://pith.science/pith/TSA2JNH2KP43MBGUOLNMFZVGQX/action/replication_record"}},"created_at":"2026-05-17T23:55:24.033367+00:00","updated_at":"2026-05-17T23:55:24.033367+00:00"}