{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:Y53LA4SF6M5B7WC7CPUXBELOT2","short_pith_number":"pith:Y53LA4SF","schema_version":"1.0","canonical_sha256":"c776b07245f33a1fd85f13e970916e9ea84a9153c07b2bb8506e5bbd727f6c72","source":{"kind":"arxiv","id":"1810.13006","version":1},"attestation_state":"computed","paper":{"title":"Rate-Efficiency and Straggler-Robustness through Partition in Distributed Two-Sided Secure Matrix Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Aydin Sezgin, Jaber Kakar, Seyedhamed Ebadifar","submitted_at":"2018-10-30T21:03:55Z","abstract_excerpt":"Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of a large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typically of concern. In this paper, we study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product $\\boldsymbol{AB}$ of two finite field private matrices $\\boldsymbol{A}$ and $\\boldsymbol{B}$ from an information-theoretic perspective. In"},"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":"1810.13006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-10-30T21:03:55Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"d8b1c814e7e91ea0f904a07dbb29e1c09ee6079689b74ad53f8d55c939717504","abstract_canon_sha256":"90cd44166fc8a4cdfb85a8bde107430d001402c39c1eb75f574afed6d5097ed1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:52.486449Z","signature_b64":"7sSawoUhuZo9HkY/mZOWI+Zbvn6RXobB9vxAiQ0mD/ofM1fMoVT2XUTbD565IXiehLrmQXZJfwTvIt6eQsvuDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c776b07245f33a1fd85f13e970916e9ea84a9153c07b2bb8506e5bbd727f6c72","last_reissued_at":"2026-05-18T00:01:52.486066Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:52.486066Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rate-Efficiency and Straggler-Robustness through Partition in Distributed Two-Sided Secure Matrix Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Aydin Sezgin, Jaber Kakar, Seyedhamed Ebadifar","submitted_at":"2018-10-30T21:03:55Z","abstract_excerpt":"Computationally efficient matrix multiplication is a fundamental requirement in various fields, including and particularly in data analytics. To do so, the computation task of a large-scale matrix multiplication is typically outsourced to multiple servers. However, due to data misusage at the servers, security is typically of concern. In this paper, we study the two-sided secure matrix multiplication problem, where a user is interested in the matrix product $\\boldsymbol{AB}$ of two finite field private matrices $\\boldsymbol{A}$ and $\\boldsymbol{B}$ from an information-theoretic perspective. In"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13006","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":"1810.13006","created_at":"2026-05-18T00:01:52.486126+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.13006v1","created_at":"2026-05-18T00:01:52.486126+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13006","created_at":"2026-05-18T00:01:52.486126+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y53LA4SF6M5B","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y53LA4SF6M5B7WC7","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y53LA4SF","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.10684","citing_title":"On the Upload versus Download Cost for Secure and Private Matrix Multiplication","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2","json":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2.json","graph_json":"https://pith.science/api/pith-number/Y53LA4SF6M5B7WC7CPUXBELOT2/graph.json","events_json":"https://pith.science/api/pith-number/Y53LA4SF6M5B7WC7CPUXBELOT2/events.json","paper":"https://pith.science/paper/Y53LA4SF"},"agent_actions":{"view_html":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2","download_json":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2.json","view_paper":"https://pith.science/paper/Y53LA4SF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.13006&json=true","fetch_graph":"https://pith.science/api/pith-number/Y53LA4SF6M5B7WC7CPUXBELOT2/graph.json","fetch_events":"https://pith.science/api/pith-number/Y53LA4SF6M5B7WC7CPUXBELOT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2/action/storage_attestation","attest_author":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2/action/author_attestation","sign_citation":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2/action/citation_signature","submit_replication":"https://pith.science/pith/Y53LA4SF6M5B7WC7CPUXBELOT2/action/replication_record"}},"created_at":"2026-05-18T00:01:52.486126+00:00","updated_at":"2026-05-18T00:01:52.486126+00:00"}