{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:F5E3VVRCWTQTFN3XQ6SHA52VWR","short_pith_number":"pith:F5E3VVRC","canonical_record":{"source":{"id":"1611.00340","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-01T19:19:49Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"87de95c56f2558f2c6cf672d98166d31f5536edc0777cfe768af0c1d9e86407b","abstract_canon_sha256":"9274f26e479385acc2c4bd15b4425aaacaf6fa73f3af97e681a358f827ede31e"},"schema_version":"1.0"},"canonical_sha256":"2f49bad622b4e132b77787a4707755b445adf70cf63cbbd1cadd82013266beec","source":{"kind":"arxiv","id":"1611.00340","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.00340","created_at":"2026-05-17T23:59:18Z"},{"alias_kind":"arxiv_version","alias_value":"1611.00340v5","created_at":"2026-05-17T23:59:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00340","created_at":"2026-05-17T23:59:18Z"},{"alias_kind":"pith_short_12","alias_value":"F5E3VVRCWTQT","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"F5E3VVRCWTQTFN3X","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"F5E3VVRC","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:F5E3VVRCWTQTFN3XQ6SHA52VWR","target":"record","payload":{"canonical_record":{"source":{"id":"1611.00340","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-01T19:19:49Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"87de95c56f2558f2c6cf672d98166d31f5536edc0777cfe768af0c1d9e86407b","abstract_canon_sha256":"9274f26e479385acc2c4bd15b4425aaacaf6fa73f3af97e681a358f827ede31e"},"schema_version":"1.0"},"canonical_sha256":"2f49bad622b4e132b77787a4707755b445adf70cf63cbbd1cadd82013266beec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:18.233531Z","signature_b64":"J5LWePUzpSfzkbk6RA6zkKnpKRU8be9by+d/A8nk4BKewyN5QVdcHkzVgOGDCG1cKPEBdGpLua3FvQmbv4J+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f49bad622b4e132b77787a4707755b445adf70cf63cbbd1cadd82013266beec","last_reissued_at":"2026-05-17T23:59:18.232955Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:18.232955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.00340","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a0KHmkQWKQ9ftYy15Jw+O2Eu1vKmHK63niy6su7DnANj/ZFYiVY4lYQC30Uw1zhwkmY/Vhs7hNRmIzvcwlY3Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T19:36:19.589518Z"},"content_sha256":"68d36039f98b2c81cd4512964139c52fe0c163b4147c476e865127cfb60f5453","schema_version":"1.0","event_id":"sha256:68d36039f98b2c81cd4512964139c52fe0c163b4147c476e865127cfb60f5453"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:F5E3VVRCWTQTFN3XQ6SHA52VWR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variational Bayes In Private Settings (VIPS)","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"stat.ML","authors_text":"James Foulds, Kamalika Chaudhuri, Max Welling, Mijung Park","submitted_at":"2016-11-01T19:19:49Z","abstract_excerpt":"Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion, and encompasses a large class of probabilistic models, called the Conjugate Exponential (CE) family. We observe that we can straightforwardly privatise VB's approximate posterior distributions for models in the CE family, by perturbing the expe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00340","kind":"arxiv","version":5},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3+CfPPMubSbHlwXpBMMwujctGPIOnn/obcGquKcl/tv60u/drR5NXAAt4UVAlygjU5VNiH3BZED5YbpHMslZDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T19:36:19.589888Z"},"content_sha256":"8d4630b971f50ea921c7fb10c85f7ad5179c205831dcca6bc396e176d95f5e7f","schema_version":"1.0","event_id":"sha256:8d4630b971f50ea921c7fb10c85f7ad5179c205831dcca6bc396e176d95f5e7f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR/bundle.json","state_url":"https://pith.science/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-04T19:36:19Z","links":{"resolver":"https://pith.science/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR","bundle":"https://pith.science/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR/bundle.json","state":"https://pith.science/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F5E3VVRCWTQTFN3XQ6SHA52VWR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:F5E3VVRCWTQTFN3XQ6SHA52VWR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9274f26e479385acc2c4bd15b4425aaacaf6fa73f3af97e681a358f827ede31e","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-01T19:19:49Z","title_canon_sha256":"87de95c56f2558f2c6cf672d98166d31f5536edc0777cfe768af0c1d9e86407b"},"schema_version":"1.0","source":{"id":"1611.00340","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.00340","created_at":"2026-05-17T23:59:18Z"},{"alias_kind":"arxiv_version","alias_value":"1611.00340v5","created_at":"2026-05-17T23:59:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.00340","created_at":"2026-05-17T23:59:18Z"},{"alias_kind":"pith_short_12","alias_value":"F5E3VVRCWTQT","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"F5E3VVRCWTQTFN3X","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"F5E3VVRC","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:8d4630b971f50ea921c7fb10c85f7ad5179c205831dcca6bc396e176d95f5e7f","target":"graph","created_at":"2026-05-17T23:59:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Many applications of Bayesian data analysis involve sensitive information, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion, and encompasses a large class of probabilistic models, called the Conjugate Exponential (CE) family. We observe that we can straightforwardly privatise VB's approximate posterior distributions for models in the CE family, by perturbing the expe","authors_text":"James Foulds, Kamalika Chaudhuri, Max Welling, Mijung Park","cross_cats":["cs.CR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-01T19:19:49Z","title":"Variational Bayes In Private Settings (VIPS)"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.00340","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:68d36039f98b2c81cd4512964139c52fe0c163b4147c476e865127cfb60f5453","target":"record","created_at":"2026-05-17T23:59:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9274f26e479385acc2c4bd15b4425aaacaf6fa73f3af97e681a358f827ede31e","cross_cats_sorted":["cs.CR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-11-01T19:19:49Z","title_canon_sha256":"87de95c56f2558f2c6cf672d98166d31f5536edc0777cfe768af0c1d9e86407b"},"schema_version":"1.0","source":{"id":"1611.00340","kind":"arxiv","version":5}},"canonical_sha256":"2f49bad622b4e132b77787a4707755b445adf70cf63cbbd1cadd82013266beec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f49bad622b4e132b77787a4707755b445adf70cf63cbbd1cadd82013266beec","first_computed_at":"2026-05-17T23:59:18.232955Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:18.232955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"J5LWePUzpSfzkbk6RA6zkKnpKRU8be9by+d/A8nk4BKewyN5QVdcHkzVgOGDCG1cKPEBdGpLua3FvQmbv4J+Bg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:18.233531Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.00340","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68d36039f98b2c81cd4512964139c52fe0c163b4147c476e865127cfb60f5453","sha256:8d4630b971f50ea921c7fb10c85f7ad5179c205831dcca6bc396e176d95f5e7f"],"state_sha256":"0381c3f60e924fa2b4d4830c0ac676913aabf3983c7c26a401a73d3cc3a57a1f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I2by3aBQKO81OPPdhJo4YjNQrH4XcQQnE0K2Q8D1BN55U0g1IwpMU3p7HABOg2vKqfNvK9Mhh88ultDj06/9DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T19:36:19.592057Z","bundle_sha256":"2792e0f6d787258a64b9b3e6e28e7e280ee2fbd7b63be88bd9ec6d9d1e66b6b7"}}