{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:SQMDGNQ2TFOKYN7TOHLA7TGNWK","short_pith_number":"pith:SQMDGNQ2","canonical_record":{"source":{"id":"2311.01295","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T15:12:12Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"c9cbf6e5f814d9b23e631aa27c7609726844c2444995086429a51baa0f814fdc","abstract_canon_sha256":"f67b37daa29199f2766312cc5ac1c43d040b0d3c3a2cdaf4037ae28eb71c8f9a"},"schema_version":"1.0"},"canonical_sha256":"941833361a995cac37f371d60fcccdb2844e64a9c3a5f6fb1ce4a1f091d7d20d","source":{"kind":"arxiv","id":"2311.01295","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.01295","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"arxiv_version","alias_value":"2311.01295v1","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.01295","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"pith_short_12","alias_value":"SQMDGNQ2TFOK","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"pith_short_16","alias_value":"SQMDGNQ2TFOKYN7T","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"pith_short_8","alias_value":"SQMDGNQ2","created_at":"2026-07-05T07:08:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:SQMDGNQ2TFOKYN7TOHLA7TGNWK","target":"record","payload":{"canonical_record":{"source":{"id":"2311.01295","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T15:12:12Z","cross_cats_sorted":["cs.CR","cs.CV"],"title_canon_sha256":"c9cbf6e5f814d9b23e631aa27c7609726844c2444995086429a51baa0f814fdc","abstract_canon_sha256":"f67b37daa29199f2766312cc5ac1c43d040b0d3c3a2cdaf4037ae28eb71c8f9a"},"schema_version":"1.0"},"canonical_sha256":"941833361a995cac37f371d60fcccdb2844e64a9c3a5f6fb1ce4a1f091d7d20d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:08:23.900591Z","signature_b64":"xahqptd6udyZ6jMIeFCtsxr3K/ofBcudneEuI6Xx5h7m+B2IS3prRfCUddOqC7UC3I2Es8VXSHFWJMGpr3NRBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"941833361a995cac37f371d60fcccdb2844e64a9c3a5f6fb1ce4a1f091d7d20d","last_reissued_at":"2026-07-05T07:08:23.900016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:08:23.900016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.01295","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:08:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xpAIekA1uV/AbWj8VwMRn1xuwHvUiZRycLhSWBMwnLgUOigoOm04ljlXFq/fyA0Lxc8N8abBj8Rfh37v9CvdBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:48:07.578525Z"},"content_sha256":"cc6957de945c33aa99051e436f8ac9076f308c36439f4a2c9dfbea717f1658b9","schema_version":"1.0","event_id":"sha256:cc6957de945c33aa99051e436f8ac9076f308c36439f4a2c9dfbea717f1658b9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:SQMDGNQ2TFOKYN7TOHLA7TGNWK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.CV"],"primary_cat":"cs.LG","authors_text":"Francesco Pittaluga, Vijay Kumar B G, Vincent Bindschaedler, Wenxuan Bao","submitted_at":"2023-11-02T15:12:12Z","abstract_excerpt":"Data augmentation techniques, such as simple image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approaches, due to the latter's built-in assumption that each training image's contribution to the learned model is bounded. In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.01295","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.01295/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:08:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w5Qcsa4lHw+e6HV/ZsB0qSUtng7R3canSXVpI+TsEQlnQfIDR0ZPzY5lVZ54yIhzDpO7fLkqj4UU8SxUla+RCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T09:48:07.579462Z"},"content_sha256":"ca6eb509dba4325aafd643fc416d3e30e2c7d413b10ba5774cbae0647ac0bc60","schema_version":"1.0","event_id":"sha256:ca6eb509dba4325aafd643fc416d3e30e2c7d413b10ba5774cbae0647ac0bc60"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK/bundle.json","state_url":"https://pith.science/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK/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":"2026-08-10T09:48:07Z","links":{"resolver":"https://pith.science/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK","bundle":"https://pith.science/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK/bundle.json","state":"https://pith.science/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SQMDGNQ2TFOKYN7TOHLA7TGNWK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:SQMDGNQ2TFOKYN7TOHLA7TGNWK","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":"f67b37daa29199f2766312cc5ac1c43d040b0d3c3a2cdaf4037ae28eb71c8f9a","cross_cats_sorted":["cs.CR","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T15:12:12Z","title_canon_sha256":"c9cbf6e5f814d9b23e631aa27c7609726844c2444995086429a51baa0f814fdc"},"schema_version":"1.0","source":{"id":"2311.01295","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.01295","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"arxiv_version","alias_value":"2311.01295v1","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.01295","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"pith_short_12","alias_value":"SQMDGNQ2TFOK","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"pith_short_16","alias_value":"SQMDGNQ2TFOKYN7T","created_at":"2026-07-05T07:08:23Z"},{"alias_kind":"pith_short_8","alias_value":"SQMDGNQ2","created_at":"2026-07-05T07:08:23Z"}],"graph_snapshots":[{"event_id":"sha256:ca6eb509dba4325aafd643fc416d3e30e2c7d413b10ba5774cbae0647ac0bc60","target":"graph","created_at":"2026-07-05T07:08:23Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.01295/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data augmentation techniques, such as simple image transformations and combinations, are highly effective at improving the generalization of computer vision models, especially when training data is limited. However, such techniques are fundamentally incompatible with differentially private learning approaches, due to the latter's built-in assumption that each training image's contribution to the learned model is bounded. In this paper, we investigate why naive applications of multi-sample data augmentation techniques, such as mixup, fail to achieve good performance and propose two novel data a","authors_text":"Francesco Pittaluga, Vijay Kumar B G, Vincent Bindschaedler, Wenxuan Bao","cross_cats":["cs.CR","cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T15:12:12Z","title":"DP-Mix: Mixup-based Data Augmentation for Differentially Private Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.01295","kind":"arxiv","version":1},"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:cc6957de945c33aa99051e436f8ac9076f308c36439f4a2c9dfbea717f1658b9","target":"record","created_at":"2026-07-05T07:08:23Z","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":"f67b37daa29199f2766312cc5ac1c43d040b0d3c3a2cdaf4037ae28eb71c8f9a","cross_cats_sorted":["cs.CR","cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-11-02T15:12:12Z","title_canon_sha256":"c9cbf6e5f814d9b23e631aa27c7609726844c2444995086429a51baa0f814fdc"},"schema_version":"1.0","source":{"id":"2311.01295","kind":"arxiv","version":1}},"canonical_sha256":"941833361a995cac37f371d60fcccdb2844e64a9c3a5f6fb1ce4a1f091d7d20d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"941833361a995cac37f371d60fcccdb2844e64a9c3a5f6fb1ce4a1f091d7d20d","first_computed_at":"2026-07-05T07:08:23.900016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:08:23.900016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xahqptd6udyZ6jMIeFCtsxr3K/ofBcudneEuI6Xx5h7m+B2IS3prRfCUddOqC7UC3I2Es8VXSHFWJMGpr3NRBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:08:23.900591Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.01295","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cc6957de945c33aa99051e436f8ac9076f308c36439f4a2c9dfbea717f1658b9","sha256:ca6eb509dba4325aafd643fc416d3e30e2c7d413b10ba5774cbae0647ac0bc60"],"state_sha256":"116787dc5a522c27ce9b0279c71992ce323d9b118234e04ab53c43f810813781"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A1uxw+aGP1eL8ILlNDF7UyvxoYNrkJQqrBw6QiXX7yLO8w1D7TyNkF8058WA3Q6+Rc1eyZoJmLovKa2TjmC7AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T09:48:07.584676Z","bundle_sha256":"b02bff073c615aa5ab71fd387641cc860d30bcd8ef53d8e74960efa24d8215e7"}}