{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/privacy-preserving/papers/5","list_of":"/task/privacy-preserving","task":"Privacy Preserving","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":5,"pages_in_order":30,"rows_per_page":100,"rows":[401,500],"of":2975,"counts":{"archive_papers_tagged":2975,"with_a_code_link":758,"where_syntology_ran_a_sample":127,"not_listed_spam_title":0,"listed":2975,"listed_where_code_ran":127,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":112,"every_run_a_failure_of_syntologys_instrument":15,"listed_with_a_run_with_no_instrument_failure":112,"listed_every_run_a_failure_of_syntologys_instrument":15,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/privacy-preserving","prev":"/task/privacy-preserving/papers/4","next":"/task/privacy-preserving/papers/6","papers":[{"url":"/paper/a-survey-for-federated-learning-evaluations","slug":"a-survey-for-federated-learning-evaluations","title":"A Survey for Federated Learning Evaluations: Goals and Measures","date":"2023-08-23","arxiv_id":"2308.11841","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-face-recognition-using","slug":"privacy-preserving-face-recognition-using","title":"Privacy-Preserving Face Recognition Using Random Frequency Components","date":"2023-08-21","arxiv_id":"2308.10461","repositories_listed":1,"syntology":null},{"url":"/paper/dpmac-differentially-private-communication","slug":"dpmac-differentially-private-communication","title":"DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning","date":"2023-08-19","arxiv_id":"2308.09902","repositories_listed":1,"syntology":null},{"url":"/paper/appflx-providing-privacy-preserving-cross","slug":"appflx-providing-privacy-preserving-cross","title":"APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service","date":"2023-08-17","arxiv_id":"2308.08786","repositories_listed":1,"syntology":null},{"url":"/paper/independent-distribution-regularization-for","slug":"independent-distribution-regularization-for","title":"Independent Distribution Regularization for Private Graph Embedding","date":"2023-08-16","arxiv_id":"2308.08360","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-few-shot-traffic-detection","slug":"privacy-preserving-few-shot-traffic-detection","title":"Privacy-preserving Few-shot Traffic Detection against Advanced Persistent Threats via Federated Meta Learning","date":"2023-08-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-learning-from-distributed-data","slug":"collaborative-learning-from-distributed-data","title":"Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data","date":"2023-08-09","arxiv_id":"2308.04755","repositories_listed":1,"syntology":null},{"url":"/paper/cross-silo-prototypical-calibration-for","slug":"cross-silo-prototypical-calibration-for","title":"Cross-Silo Prototypical Calibration for Federated Learning with Non-IID Data","date":"2023-08-07","arxiv_id":"2308.03457","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/cross-silo-prototypical-calibration-for#ran","syntology_url":"https://syntology.ai/paper/2308.03457","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.03457"}},"official":{"repos":["qizhuang-qz/fedcspc"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/randomized-algorithms-for-precise-measurement","slug":"randomized-algorithms-for-precise-measurement","title":"Randomized algorithms for precise measurement of differentially-private, personalized recommendations","date":"2023-08-07","arxiv_id":"2308.03735","repositories_listed":1,"syntology":null},{"url":"/paper/a-differentially-private-weighted-empirical","slug":"a-differentially-private-weighted-empirical","title":"A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning","date":"2023-07-24","arxiv_id":"2307.13127","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-patient-clustering-for","slug":"privacy-preserving-patient-clustering-for","title":"Privacy-preserving patient clustering for personalized federated learning","date":"2023-07-17","arxiv_id":"2307.08847","repositories_listed":1,"syntology":null},{"url":"/paper/a-comprehensive-survey-of-forgetting-in-deep","slug":"a-comprehensive-survey-of-forgetting-in-deep","title":"A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning","date":"2023-07-16","arxiv_id":"2307.09218","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-utility-gain-of-iterative-bayesian","slug":"on-the-utility-gain-of-iterative-bayesian","title":"On the Utility Gain of Iterative Bayesian Update for Locally Differentially Private Mechanisms","date":"2023-07-15","arxiv_id":"2307.07744","repositories_listed":1,"syntology":null},{"url":"/paper/a-privacy-preserving-walk-in-the-latent-space","slug":"a-privacy-preserving-walk-in-the-latent-space","title":"A Privacy-Preserving Walk in the Latent Space of Generative Models for Medical Applications","date":"2023-07-06","arxiv_id":"2307.02984","repositories_listed":1,"syntology":null},{"url":"/paper/dpm-clustering-sensitive-data-through","slug":"dpm-clustering-sensitive-data-through","title":"DPM: Clustering Sensitive Data through Separation","date":"2023-07-06","arxiv_id":"2307.02969","repositories_listed":1,"syntology":null},{"url":"/paper/a-synthetic-electrocardiogram-ecg-image","slug":"a-synthetic-electrocardiogram-ecg-image","title":"ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization","date":"2023-07-04","arxiv_id":"2307.01946","repositories_listed":1,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":3,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 3 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/a-synthetic-electrocardiogram-ecg-image#ran","syntology_url":"https://syntology.ai/paper/2307.01946","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.01946"}},"official":{"repos":["alphanumericslab/ecg-image-kit"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/feddefender-backdoor-attack-defense-in","slug":"feddefender-backdoor-attack-defense-in","title":"FedDefender: Backdoor Attack Defense in Federated Learning","date":"2023-07-02","arxiv_id":"2307.08672","repositories_listed":1,"syntology":null},{"url":"/paper/long-term-conversation-analysis-exploring","slug":"long-term-conversation-analysis-exploring","title":"Long-term Conversation Analysis: Exploring Utility and Privacy","date":"2023-06-28","arxiv_id":"2306.16071","repositories_listed":1,"syntology":null},{"url":"/paper/nipd-a-federated-learning-person-detection","slug":"nipd-a-federated-learning-person-detection","title":"NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data","date":"2023-06-28","arxiv_id":"2306.15932","repositories_listed":1,"syntology":null},{"url":"/paper/video-object-detection-for-privacy-preserving","slug":"video-object-detection-for-privacy-preserving","title":"Video object detection for privacy-preserving patient monitoring in intensive care","date":"2023-06-26","arxiv_id":"2306.14620","repositories_listed":1,"syntology":null},{"url":"/paper/mimic-combating-client-dropouts-in-federated","slug":"mimic-combating-client-dropouts-in-federated","title":"MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates","date":"2023-06-21","arxiv_id":"2306.12212","repositories_listed":1,"syntology":null},{"url":"/paper/mmasd-a-multimodal-dataset-for-autism","slug":"mmasd-a-multimodal-dataset-for-autism","title":"MMASD: A Multimodal Dataset for Autism Intervention Analysis","date":"2023-06-14","arxiv_id":"2306.08243","repositories_listed":1,"syntology":null},{"url":"/paper/plan-variance-aware-private-mean-estimation","slug":"plan-variance-aware-private-mean-estimation","title":"PLAN: Variance-Aware Private Mean Estimation","date":"2023-06-14","arxiv_id":"2306.08745","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/plan-variance-aware-private-mean-estimation#ran","syntology_url":"https://syntology.ai/paper/2306.08745","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.08745"}},"official":{"repos":["christianlebeda/plan-experiments"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/safeguarding-data-in-multimodal-ai-a","slug":"safeguarding-data-in-multimodal-ai-a","title":"Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training","date":"2023-06-13","arxiv_id":"2306.08173","repositories_listed":1,"syntology":null},{"url":"/paper/does-image-anonymization-impact-computer","slug":"does-image-anonymization-impact-computer","title":"Does Image Anonymization Impact Computer Vision Training?","date":"2023-06-08","arxiv_id":"2306.05135","repositories_listed":1,"syntology":null},{"url":"/paper/human-imperceptible-machine-recognizable","slug":"human-imperceptible-machine-recognizable","title":"Human-imperceptible, Machine-recognizable Images","date":"2023-06-06","arxiv_id":"2306.03679","repositories_listed":1,"syntology":null},{"url":"/paper/a-fair-platform-for-reproducing-mutational","slug":"a-fair-platform-for-reproducing-mutational","title":"A FAIR platform for reproducing mutational signature detection on tumor sequencing data","date":"2023-06-02","arxiv_id":"2306.01634","repositories_listed":1,"syntology":null},{"url":"/paper/non-uniform-speaker-disentanglement-for","slug":"non-uniform-speaker-disentanglement-for","title":"Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals","date":"2023-06-02","arxiv_id":"2306.01861","repositories_listed":1,"syntology":null},{"url":"/paper/merge-fast-private-text-generation","slug":"merge-fast-private-text-generation","title":"MERGE: Fast Private Text Generation","date":"2023-05-25","arxiv_id":"2305.15769","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-small-medical-learners-with-privacy","slug":"enhancing-small-medical-learners-with-privacy","title":"Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting","date":"2023-05-22","arxiv_id":"2305.12723","repositories_listed":1,"syntology":null},{"url":"/paper/graph-guided-personalization-for-federated","slug":"graph-guided-personalization-for-federated","title":"GPFedRec: Graph-guided Personalization for Federated Recommendation","date":"2023-05-13","arxiv_id":"2305.07866","repositories_listed":1,"syntology":null},{"url":"/paper/indoor-localization-and-multi-person-tracking","slug":"indoor-localization-and-multi-person-tracking","title":"A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing","date":"2023-05-08","arxiv_id":"2305.05062","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-representations-are-not","slug":"privacy-preserving-representations-are-not","title":"Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses","date":"2023-05-08","arxiv_id":"2305.04603","repositories_listed":1,"syntology":null},{"url":"/paper/gradient-leakage-defense-with-key-lock-module","slug":"gradient-leakage-defense-with-key-lock-module","title":"Gradient Leakage Defense with Key-Lock Module for Federated Learning","date":"2023-05-06","arxiv_id":"2305.04095","repositories_listed":1,"syntology":null},{"url":"/paper/adversarially-guided-portrait-matting","slug":"adversarially-guided-portrait-matting","title":"Adversarially-Guided Portrait Matting","date":"2023-05-04","arxiv_id":"2305.02981","repositories_listed":1,"syntology":null},{"url":"/paper/local-differential-privacy-has-no-disparate","slug":"local-differential-privacy-has-no-disparate","title":"(Local) Differential Privacy has NO Disparate Impact on Fairness","date":"2023-04-25","arxiv_id":"2304.12845","repositories_listed":1,"syntology":null},{"url":"/paper/practical-differentially-private-and","slug":"practical-differentially-private-and","title":"Practical Differentially Private and Byzantine-resilient Federated Learning","date":"2023-04-15","arxiv_id":"2304.09762","repositories_listed":1,"syntology":null},{"url":"/paper/you-are-here-finding-position-and-orientation","slug":"you-are-here-finding-position-and-orientation","title":"3DoF Localization from a Single Image and an Object Map: the Flatlandia Problem and Dataset","date":"2023-04-13","arxiv_id":"2304.06373","repositories_listed":1,"syntology":null},{"url":"/paper/reinforcement-learning-based-black-box-model","slug":"reinforcement-learning-based-black-box-model","title":"Reinforcement Learning-Based Black-Box Model Inversion Attacks","date":"2023-04-10","arxiv_id":"2304.04625","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-cnn-training-with-transfer","slug":"privacy-preserving-cnn-training-with-transfer","title":"Privacy-Preserving CNN Training with Transfer Learning: Multiclass Logistic Regression","date":"2023-04-07","arxiv_id":"2304.03807","repositories_listed":1,"syntology":null},{"url":"/paper/selective-knowledge-sharing-for-privacy","slug":"selective-knowledge-sharing-for-privacy","title":"Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher","date":"2023-04-04","arxiv_id":"2304.01731","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/selective-knowledge-sharing-for-privacy#ran","syntology_url":"https://syntology.ai/paper/2304.01731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01731"}},"official":{"repos":["shaojiawei07/selective-fd"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-federated-learning-on-long","slug":"personalized-federated-learning-on-long","title":"Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentation","date":"2023-03-27","arxiv_id":"2303.15168","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-algorithms-for-3","slug":"differentially-private-algorithms-for-3","title":"Differentially Private Algorithms for Synthetic Power System Datasets","date":"2023-03-20","arxiv_id":"2303.11079","repositories_listed":1,"syntology":null},{"url":"/paper/experimenting-with-normalization-layers-in","slug":"experimenting-with-normalization-layers-in","title":"Experimenting with Normalization Layers in Federated Learning on non-IID scenarios","date":"2023-03-19","arxiv_id":"2303.10630","repositories_listed":1,"syntology":null},{"url":"/paper/schrodinger-s-camera-first-steps-towards-a","slug":"schrodinger-s-camera-first-steps-towards-a","title":"Schrödinger's Camera: First Steps Towards a Quantum-Based Privacy Preserving Camera","date":"2023-03-13","arxiv_id":"2303.07510","repositories_listed":1,"syntology":null},{"url":"/paper/multi-metrics-adaptively-identifies-backdoors","slug":"multi-metrics-adaptively-identifies-backdoors","title":"Multi-metrics adaptively identifies backdoors in Federated learning","date":"2023-03-12","arxiv_id":"2303.06601","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":1,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-metrics-adaptively-identifies-backdoors#ran","syntology_url":"https://syntology.ai/paper/2303.06601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06601"}},"official":{"repos":["siquanhuang/Multi-metrics_against_backdoors_in_FL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dp-fast-mh-private-fast-and-accurate","slug":"dp-fast-mh-private-fast-and-accurate","title":"DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference","date":"2023-03-10","arxiv_id":"2303.06171","repositories_listed":1,"syntology":null},{"url":"/paper/fedscore-a-privacy-preserving-framework-for","slug":"fedscore-a-privacy-preserving-framework-for","title":"FedScore: A privacy-preserving framework for federated scoring system development","date":"2023-03-01","arxiv_id":"2303.00282","repositories_listed":1,"syntology":null},{"url":"/paper/arbitrary-decisions-are-a-hidden-cost-of","slug":"arbitrary-decisions-are-a-hidden-cost-of","title":"Arbitrary Decisions are a Hidden Cost of Differentially Private Training","date":"2023-02-28","arxiv_id":"2302.14517","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/arbitrary-decisions-are-a-hidden-cost-of#ran","syntology_url":"https://syntology.ai/paper/2302.14517","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14517"}},"official":{"repos":["spring-epfl/dp_multiplicity"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fedclip-fast-generalization-and","slug":"fedclip-fast-generalization-and","title":"FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning","date":"2023-02-27","arxiv_id":"2302.13485","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/fedclip-fast-generalization-and#ran","syntology_url":"https://syntology.ai/paper/2302.13485","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.13485"}},"official":{"repos":["microsoft/personalizedfl"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/active-membership-inference-attack-under","slug":"active-membership-inference-attack-under","title":"Active Membership Inference Attack under Local Differential Privacy in Federated Learning","date":"2023-02-24","arxiv_id":"2302.12685","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/active-membership-inference-attack-under#ran","syntology_url":"https://syntology.ai/paper/2302.12685","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.12685"}},"official":{"repos":["trucndt/ami"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/personalized-privacy-preserving-framework-for","slug":"personalized-privacy-preserving-framework-for","title":"Personalized Privacy-Preserving Framework for Cross-Silo Federated Learning","date":"2023-02-22","arxiv_id":"2302.12020","repositories_listed":1,"syntology":null},{"url":"/paper/audit-to-forget-a-unified-method-to-revoke","slug":"audit-to-forget-a-unified-method-to-revoke","title":"Audit to Forget: A Unified Method to Revoke Patients' Private Data in Intelligent Healthcare","date":"2023-02-20","arxiv_id":"2302.09813","repositories_listed":1,"syntology":null},{"url":"/paper/personalized-and-privacy-preserving-federated","slug":"personalized-and-privacy-preserving-federated","title":"Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI","date":"2023-02-20","arxiv_id":"2302.11571","repositories_listed":1,"syntology":null},{"url":"/paper/metropolitan-segment-traffic-speeds-from","slug":"metropolitan-segment-traffic-speeds-from","title":"Metropolitan Segment Traffic Speeds from Massive Floating Car Data in 10 Cities","date":"2023-02-17","arxiv_id":"2302.08761","repositories_listed":1,"syntology":null},{"url":"/paper/he-man-homomorphically-encrypted-machine","slug":"he-man-homomorphically-encrypted-machine","title":"HE-MAN -- Homomorphically Encrypted MAchine learning with oNnx models","date":"2023-02-16","arxiv_id":"2302.08260","repositories_listed":1,"syntology":null},{"url":"/paper/a-federated-learning-benchmark-for-drug","slug":"a-federated-learning-benchmark-for-drug","title":"A Federated Learning Benchmark for Drug-Target Interaction","date":"2023-02-15","arxiv_id":"2302.07684","repositories_listed":1,"syntology":null},{"url":"/paper/deep-neural-networks-for-encrypted-inference","slug":"deep-neural-networks-for-encrypted-inference","title":"Deep Neural Networks for Encrypted Inference with TFHE","date":"2023-02-13","arxiv_id":"2302.10906","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/deep-neural-networks-for-encrypted-inference#ran","syntology_url":"https://syntology.ai/paper/2302.10906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10906"}},"official":{"repos":["zama-ai/concrete-ml"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/privacy-preserving-tree-based-inference-with","slug":"privacy-preserving-tree-based-inference-with","title":"Privacy-Preserving Tree-Based Inference with TFHE","date":"2023-02-13","arxiv_id":"2303.01254","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-normalizing-flows-for-1","slug":"differentially-private-normalizing-flows-for-1","title":"Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records","date":"2023-02-11","arxiv_id":"2302.05787","repositories_listed":1,"syntology":null},{"url":"/paper/offsite-tuning-transfer-learning-without-full","slug":"offsite-tuning-transfer-learning-without-full","title":"Offsite-Tuning: Transfer Learning without Full Model","date":"2023-02-09","arxiv_id":"2302.04870","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/offsite-tuning-transfer-learning-without-full#ran","syntology_url":"https://syntology.ai/paper/2302.04870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.04870"}},"official":{"repos":["mit-han-lab/offsite-tuning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/a-privacy-preserving-hybrid-federated","slug":"a-privacy-preserving-hybrid-federated","title":"A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection","date":"2023-02-07","arxiv_id":"2302.03654","repositories_listed":1,"syntology":null},{"url":"/paper/federated-survival-forests","slug":"federated-survival-forests","title":"Federated Survival Forests","date":"2023-02-06","arxiv_id":"2302.02807","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/federated-survival-forests#ran","syntology_url":"https://syntology.ai/paper/2302.02807","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02807"}},"official":{"repos":["archettialberto/federated_survival_forests"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/differentially-private-distributed-bayesian","slug":"differentially-private-distributed-bayesian","title":"Differentially Private Distributed Bayesian Linear Regression with MCMC","date":"2023-01-31","arxiv_id":"2301.13778","repositories_listed":1,"syntology":null},{"url":"/paper/extracting-training-data-from-diffusion","slug":"extracting-training-data-from-diffusion","title":"Extracting Training Data from Diffusion Models","date":"2023-01-30","arxiv_id":"2301.13188","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/extracting-training-data-from-diffusion#ran","syntology_url":"https://syntology.ai/paper/2301.13188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13188"}},"official":null}},{"url":"/paper/private-node-selection-in-personalized","slug":"private-node-selection-in-personalized","title":"Efficient Node Selection in Private Personalized Decentralized Learning","date":"2023-01-30","arxiv_id":"2301.12755","repositories_listed":1,"syntology":null},{"url":"/paper/fedhp-heterogeneous-federated-learning-with","slug":"fedhp-heterogeneous-federated-learning-with","title":"FedPH: Privacy-enhanced Heterogeneous Federated Learning","date":"2023-01-27","arxiv_id":"2301.11705","repositories_listed":1,"syntology":null},{"url":"/paper/split-ways-privacy-preserving-training-of","slug":"split-ways-privacy-preserving-training-of","title":"Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning","date":"2023-01-20","arxiv_id":"2301.08778","repositories_listed":1,"syntology":null},{"url":"/paper/differentially-private-online-bayesian","slug":"differentially-private-online-bayesian","title":"Differentially Private Online Bayesian Estimation With Adaptive Truncation","date":"2023-01-19","arxiv_id":"2301.08202","repositories_listed":1,"syntology":null},{"url":"/paper/dual-personalization-on-federated","slug":"dual-personalization-on-federated","title":"Dual Personalization on Federated Recommendation","date":"2023-01-16","arxiv_id":"2301.08143","repositories_listed":1,"syntology":null},{"url":"/paper/pmp-privacy-aware-matrix-profile-against","slug":"pmp-privacy-aware-matrix-profile-against","title":"PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time Series","date":"2023-01-04","arxiv_id":"2301.01838","repositories_listed":1,"syntology":null},{"url":"/paper/dartblur-privacy-preservation-with-detection","slug":"dartblur-privacy-preservation-with-detection","title":"DartBlur: Privacy Preservation With Detection Artifact Suppression","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/elastic-aggregation-for-federated","slug":"elastic-aggregation-for-federated","title":"Elastic Aggregation for Federated Optimization","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/federated-domain-generalization-with","slug":"federated-domain-generalization-with","title":"Federated Domain Generalization With Generalization Adjustment","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/global-balanced-experts-for-federated-long","slug":"global-balanced-experts-for-federated-long","title":"Global Balanced Experts for Federated Long-Tailed Learning","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/paired-point-lifting-for-enhanced-privacy","slug":"paired-point-lifting-for-enhanced-privacy","title":"Paired-Point Lifting for Enhanced Privacy-Preserving Visual Localization","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-representations-are-not-1","slug":"privacy-preserving-representations-are-not-1","title":"Privacy-Preserving Representations Are Not Enough: Recovering Scene Content From Camera Poses","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/densepose-from-wifi","slug":"densepose-from-wifi","title":"DensePose From WiFi","date":"2022-12-31","arxiv_id":"2301.00250","repositories_listed":1,"syntology":null},{"url":"/paper/federated-pca-on-grassmann-manifold-for","slug":"federated-pca-on-grassmann-manifold-for","title":"Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks","date":"2022-12-23","arxiv_id":"2212.12121","repositories_listed":1,"syntology":null},{"url":"/paper/planting-and-mitigating-memorized-content-in","slug":"planting-and-mitigating-memorized-content-in","title":"Planting and Mitigating Memorized Content in Predictive-Text Language Models","date":"2022-12-16","arxiv_id":"2212.08619","repositories_listed":1,"syntology":null},{"url":"/paper/federated-nlp-in-few-shot-scenarios","slug":"federated-nlp-in-few-shot-scenarios","title":"Federated Few-Shot Learning for Mobile NLP","date":"2022-12-12","arxiv_id":"2212.05974","repositories_listed":1,"syntology":null},{"url":"/paper/vicious-classifiers-data-reconstruction","slug":"vicious-classifiers-data-reconstruction","title":"Vicious Classifiers: Assessing Inference-time Data Reconstruction Risk in Edge Computing","date":"2022-12-08","arxiv_id":"2212.04223","repositories_listed":1,"syntology":null},{"url":"/paper/a-study-on-extracting-named-entities-from","slug":"a-study-on-extracting-named-entities-from","title":"Memorization of Named Entities in Fine-tuned BERT Models","date":"2022-12-07","arxiv_id":"2212.03749","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-visual-localization-with","slug":"privacy-preserving-visual-localization-with","title":"Privacy-Preserving Visual Localization with Event Cameras","date":"2022-12-04","arxiv_id":"2212.03177","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-data-synthetisation-for","slug":"privacy-preserving-data-synthetisation-for","title":"Differentially-Private Data Synthetisation for Efficient Re-Identification Risk Control","date":"2022-12-01","arxiv_id":"2212.00484","repositories_listed":1,"syntology":null},{"url":"/paper/collaborative-training-of-medical-artificial","slug":"collaborative-training-of-medical-artificial","title":"Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels","date":"2022-11-24","arxiv_id":"2211.13606","repositories_listed":1,"syntology":null},{"url":"/paper/secure-and-privacy-preserving-automated-end","slug":"secure-and-privacy-preserving-automated-end","title":"Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction","date":"2022-11-13","arxiv_id":"2211.07643","repositories_listed":1,"syntology":null},{"url":"/paper/towards-privacy-aware-causal-structure","slug":"towards-privacy-aware-causal-structure","title":"Towards Privacy-Aware Causal Structure Learning in Federated Setting","date":"2022-11-13","arxiv_id":"2211.06919","repositories_listed":1,"syntology":null},{"url":"/paper/modular-clinical-decision-support-networks","slug":"modular-clinical-decision-support-networks","title":"Modular Clinical Decision Support Networks (MoDN) -- Updatable, Interpretable, and Portable Predictions for Evolving Clinical Environments","date":"2022-11-12","arxiv_id":"2211.06637","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-credit-card-fraud","slug":"privacy-preserving-credit-card-fraud","title":"Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption","date":"2022-11-12","arxiv_id":"2211.06675","repositories_listed":1,"syntology":null},{"url":"/paper/hfedms-heterogeneous-federated-learning-with","slug":"hfedms-heterogeneous-federated-learning-with","title":"HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse","date":"2022-11-07","arxiv_id":"2211.03300","repositories_listed":1,"syntology":null},{"url":"/paper/medical-diffusion-denoising-diffusion","slug":"medical-diffusion-denoising-diffusion","title":"Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation","date":"2022-11-07","arxiv_id":"2211.03364","repositories_listed":1,"syntology":null},{"url":"/paper/privacy-preserving-models-for-legal-natural","slug":"privacy-preserving-models-for-legal-natural","title":"Privacy-Preserving Models for Legal Natural Language Processing","date":"2022-11-05","arxiv_id":"2211.02956","repositories_listed":1,"syntology":null},{"url":"/paper/fedtp-federated-learning-by-transformer","slug":"fedtp-federated-learning-by-transformer","title":"FedTP: Federated Learning by Transformer Personalization","date":"2022-11-03","arxiv_id":"2211.01572","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":3,"n_honours":1,"n_violates":2,"n_no_contract":2,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 2 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/fedtp-federated-learning-by-transformer#ran","syntology_url":"https://syntology.ai/paper/2211.01572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.01572"}},"official":{"repos":["zhyczy/fedtp"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/privacy-preserving-non-negative-matrix","slug":"privacy-preserving-non-negative-matrix","title":"Privacy-preserving Non-negative Matrix Factorization with Outliers","date":"2022-11-02","arxiv_id":"2211.01451","repositories_listed":1,"syntology":null},{"url":"/paper/vertibayes-learning-bayesian-network","slug":"vertibayes-learning-bayesian-network","title":"VertiBayes: Learning Bayesian network parameters from vertically partitioned data with missing values","date":"2022-10-31","arxiv_id":"2210.17228","repositories_listed":1,"syntology":null},{"url":"/paper/private-and-reliable-neural-network-inference","slug":"private-and-reliable-neural-network-inference","title":"Private and Reliable Neural Network Inference","date":"2022-10-27","arxiv_id":"2210.15614","repositories_listed":1,"syntology":null},{"url":"/paper/nvidia-flare-federated-learning-from","slug":"nvidia-flare-federated-learning-from","title":"NVIDIA FLARE: Federated Learning from Simulation to Real-World","date":"2022-10-24","arxiv_id":"2210.13291","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/nvidia-flare-federated-learning-from#ran","syntology_url":"https://syntology.ai/paper/2210.13291","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13291"}},"official":{"repos":["nvidia/nvflare"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/privacy-preserved-neural-graph-similarity","slug":"privacy-preserved-neural-graph-similarity","title":"Privacy-Preserved Neural Graph Similarity Learning","date":"2022-10-21","arxiv_id":"2210.11730","repositories_listed":1,"syntology":null},{"url":"/paper/private-algorithms-with-private-predictions","slug":"private-algorithms-with-private-predictions","title":"Learning-Augmented Private Algorithms for Multiple Quantile Release","date":"2022-10-20","arxiv_id":"2210.11222","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/private-algorithms-with-private-predictions#ran","syntology_url":"https://syntology.ai/paper/2210.11222","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.11222"}},"official":{"repos":["mkhodak/private-quantiles"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}}],"record_sha256":"cf1247ca64aa45b133de7efadb0d8dcf414394022ef022ca38262f572f9da222","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}