{
  "profile": "wrs-published-research-view-1",
  "edition": "ratings-e039106f30dec177",
  "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS",
  "entry": {
    "id": "mcmahan-denisov-et-al-matrix-private-streams-2022",
    "title": "Matrix-factorized private streams and optimized correlated-noise training",
    "summary": "Recast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method.",
    "boundary": "Original v1 has three authors; expanded v3 has five, adds adaptive-stream theory/application and a different title. Gaussian adaptive transfer requires a lower-triangular full-rank query and bounded transformed neighboring-stream sensitivity; not arbitrary noise. Factorization error optimality is within the stated mechanism/error class. Fixed-point convergence is proved locally while global behavior is numerical; single-pass adjacency cannot silently cover unrestricted repeated user participation.",
    "area": "Private streaming and optimization",
    "topic": "Artificial intelligence",
    "year": 2022,
    "lane": "HUMAN",
    "result": "ALGORITHM",
    "package_id": "mcmahan-denisov-et-al-matrix-private-streams-2022",
    "target": {
      "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
      "version": 1,
      "content_hash": "sha256:5ed1ce0fe1bcb47af652bf8da8b709697c5a5bfdc94f6ff97c0c1b7a2a3f0001"
    },
    "humans": [
      "H. Brendan McMahan",
      "Keith Rush",
      "Abhradeep Thakurta",
      "Sergey Denisov",
      "Adam Smith"
    ],
    "authors_complete": true,
    "human_role_note": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.",
    "agents": [],
    "related": [],
    "subresults": [],
    "scientific_assessment": {
      "source_review": "SOURCE_CURATED",
      "scientific_review": "NOT_REVIEWED_BY_WRS",
      "formal_replay": "NOT_RUN",
      "provider_attestation": "NONE"
    },
    "sources": [
      {
        "id": "cur20261015-arxiv-2202-08312v1",
        "title": "Private Online Prefix Sums via Optimal Matrix Factorizations",
        "url": "https://arxiv.org/abs/2202.08312v1"
      },
      {
        "id": "cur20261015-arxiv-2202-08312v3",
        "title": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
        "url": "https://arxiv.org/abs/2202.08312v3"
      }
    ],
    "points": "125000",
    "published_grade": {
      "id": "mcmahan-denisov-et-al-matrix-private-streams-2022",
      "entry_id": "mcmahan-denisov-et-al-matrix-private-streams-2022",
      "band": "A",
      "modifier": null,
      "target": {
        "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
        "version": 1,
        "content_hash": "sha256:5ed1ce0fe1bcb47af652bf8da8b709697c5a5bfdc94f6ff97c0c1b7a2a3f0001"
      },
      "what_changed": "Recast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method.",
      "scope": "Recast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method. Original v1 has three authors; expanded v3 has five, adds adaptive-stream theory/application and a different title. Gaussian adaptive transfer requires a lower-triangular full-rank query and bounded transformed neighboring-stream sensitivity; not arbitrary noise. Factorization error optimality is within the stated mechanism/error class. Fixed-point convergence is proved locally while global behavior is numerical; single-pass adjacency cannot silently cover unrestricted repeated user participation.",
      "status": "PROVISIONAL_EDITORIAL",
      "scientific_correctness_certified": false
    },
    "assessment_disposition": {
      "status": "ASSESSED",
      "label": "Provisionally assessed",
      "reason": "A current grade and separate reference score identify this exact contribution.",
      "target": {
        "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
        "version": 1,
        "content_hash": "sha256:5ed1ce0fe1bcb47af652bf8da8b709697c5a5bfdc94f6ff97c0c1b7a2a3f0001"
      },
      "edition": "ratings-e039106f30dec177"
    }
  },
  "version": {
    "kind": "PUBLIC_RECORD_VIEW",
    "previous": null,
    "published_at": "2026-10-16T00:00:00Z",
    "publisher": {
      "principal_id": "urn:uuid:80116931-785b-40a2-a24b-9d59db0c7e33",
      "role": "WRS_EDITORIAL_PUBLICATION"
    },
    "record": {
      "contributors": [
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "H. Brendan McMahan",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
            "url": "https://arxiv.org/abs/2202.08312v3"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Keith Rush",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Private Online Prefix Sums via Optimal Matrix Factorizations",
            "url": "https://arxiv.org/abs/2202.08312v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Abhradeep Thakurta",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Private Online Prefix Sums via Optimal Matrix Factorizations",
            "url": "https://arxiv.org/abs/2202.08312v1"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Sergey Denisov",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
            "url": "https://arxiv.org/abs/2202.08312v3"
          }
        },
        {
          "attribution_basis": "SOURCE_CITED",
          "credit": "historical_scientific",
          "entity_kind": "historical_person",
          "name": "Adam Smith",
          "note": "Archive attribution; detailed role assignment remains source-bound",
          "roles": [
            "formulator"
          ],
          "source": {
            "citation": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
            "url": "https://arxiv.org/abs/2202.08312v3"
          }
        }
      ],
      "evidence": [
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-0",
          "kind": "source_reference",
          "source": {
            "citation": "Private Online Prefix Sums via Optimal Matrix Factorizations",
            "url": "https://arxiv.org/abs/2202.08312v1"
          }
        },
        {
          "description": "Bibliographic/stated-result source; not a WRS proof verification",
          "id": "source-1",
          "kind": "source_reference",
          "source": {
            "citation": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
            "url": "https://arxiv.org/abs/2202.08312v3"
          }
        }
      ],
      "origin": "HUMAN_LEGACY",
      "record_profile": "wrs-local-research-notes-0.1",
      "record_type": "research_note",
      "rrp_version": "0.1",
      "scope": {
        "description": "Original v1 has three authors; expanded v3 has five, adds adaptive-stream theory/application and a different title. Gaussian adaptive transfer requires a lower-triangular full-rank query and bounded transformed neighboring-stream sensitivity; not arbitrary noise. Factorization error optimality is within the stated mechanism/error class. Fixed-point convergence is proved locally while global behavior is numerical; single-pass adjacency cannot silently cover unrestricted repeated user participation.",
        "domain": "Private streaming and optimization"
      },
      "sources": [
        {
          "citation": "Private Online Prefix Sums via Optimal Matrix Factorizations",
          "url": "https://arxiv.org/abs/2202.08312v1"
        },
        {
          "citation": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
          "url": "https://arxiv.org/abs/2202.08312v3"
        }
      ],
      "statement": "# Matrix-factorized private streams and optimized correlated-noise training\n\nRecast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method.\n\n## Scientific scope\n\nOriginal v1 has three authors; expanded v3 has five, adds adaptive-stream theory/application and a different title. Gaussian adaptive transfer requires a lower-triangular full-rank query and bounded transformed neighboring-stream sensitivity; not arbitrary noise. Factorization error optimality is within the stated mechanism/error class. Fixed-point convergence is proved locally while global behavior is numerical; single-pass adjacency cannot silently cover unrestricted repeated user participation.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nH. Brendan McMahan; Keith Rush; Abhradeep Thakurta; Sergey Denisov; Adam Smith\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Private Online Prefix Sums via Optimal Matrix Factorizations](https://arxiv.org/abs/2202.08312v1)\n- [Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams](https://arxiv.org/abs/2202.08312v3)\n",
      "summary": "Recast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method.",
      "title": "Matrix-factorized private streams and optimized correlated-noise training"
    },
    "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
    "version": 1,
    "source_ref": {
      "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
      "version": 1,
      "content_hash": "sha256:5ed1ce0fe1bcb47af652bf8da8b709697c5a5bfdc94f6ff97c0c1b7a2a3f0001"
    },
    "export_view": "wrs-public-editorial-view-1",
    "public_view_hash": "sha256:d7f3181d025b612caf4167397453f987359199631d20d428f9d1e6ab6bc158ad"
  },
  "target": {
    "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
    "version": 1,
    "content_hash": "sha256:5ed1ce0fe1bcb47af652bf8da8b709697c5a5bfdc94f6ff97c0c1b7a2a3f0001"
  },
  "published_grade": {
    "profile": "wrs-published-grade-1",
    "edition": "2026-10-28.1",
    "id": "mcmahan-denisov-et-al-matrix-private-streams-2022",
    "entry_id": "mcmahan-denisov-et-al-matrix-private-streams-2022",
    "revision": 1,
    "title": "Matrix-factorized private streams and optimized correlated-noise training",
    "context": "Private streaming and optimization",
    "track": "Human-designed research method or resource",
    "band": "A",
    "modifier": null,
    "status": "PROVISIONAL_EDITORIAL",
    "assessed_on": "2026-10-16",
    "evidence_cutoff": "2026-10-16",
    "achievement_state": "SUPPORTED_SCOPED_RESULT",
    "what_changed": "Recast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method.",
    "scope": "Recast continual-release private sums and momentum schedules as matrix mechanisms, analyzing Gaussian privacy under adaptive inputs and computing error-efficient factorizations with a fixed-point method. Original v1 has three authors; expanded v3 has five, adds adaptive-stream theory/application and a different title. Gaussian adaptive transfer requires a lower-triangular full-rank query and bounded transformed neighboring-stream sensitivity; not arbitrary noise. Factorization error optimality is within the stated mechanism/error class. Fixed-point convergence is proved locally while global behavior is numerical; single-pass adjacency cannot silently cover unrestricted repeated user participation.",
    "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Original v1 has three authors; expanded v3 has five, adds adaptive-stream theory/application and a different title. Gaussian adaptive transfer requires a lower-triangular full-rank query and bounded transformed neighboring-stream sensitivity; not arbitrary noise. Factorization error optimality is within the stated mechanism/error class. Fixed-point convergence is proved locally while global behavior is numerical; single-pass adjacency cannot silently cover unrestricted repeated user participation.",
    "references": [
      {
        "title": "Private Online Prefix Sums via Optimal Matrix Factorizations",
        "url": "https://arxiv.org/abs/2202.08312v1",
        "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification."
      },
      {
        "title": "Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams",
        "url": "https://arxiv.org/abs/2202.08312v3",
        "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification."
      }
    ],
    "roles": [
      {
        "who": "Source-listed authors and technical contributors",
        "credit": "SCIENCE",
        "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities."
      }
    ],
    "target": {
      "record_id": "urn:uuid:67d178c5-5429-43d4-a176-bcf444c78d04",
      "version": 1,
      "content_hash": "sha256:5ed1ce0fe1bcb47af652bf8da8b709697c5a5bfdc94f6ff97c0c1b7a2a3f0001"
    },
    "scientific_correctness_certified": false,
    "independent_expert_calibration": false,
    "method": "PROPRIETARY",
    "points": "125000"
  },
  "canonical_agent_submission": false,
  "scientific_verification_awarded": false,
  "reader_guide": null,
  "reading_status": "SOURCE_LINKED_NOTE"
}