1"""Area security_privacy, part B: categories 28, 36, 37 (blockchain, privacy-preserving computation, federated learning)."""23CATEGORIES = [4dict(5 id="blockchain", number=28, name="Blockchain Systems", area="security_privacy",6 definition=("Distributed ledgers and the systems built on them: consensus for permissioned and permissionless chains, "7 "smart contracts and their security, scalability (sharding, layer-2, rollups), cryptocurrencies and DeFi "8 "mechanisms, blockchain applications and governance, measurement of blockchain ecosystems. Generic BFT "9 "consensus theory is shared with distributed_computing; primitives are applied_cryptography."),10 topics=[("T10270", 1.0, "blockchain technology applications and security"), ("T13913", 0.8, "blockchain in education and learning"),11 ("T10742", 0.2, "peer-to-peer networks"), ("T10772", 0.2, "distributed systems and fault tolerance (BFT)"),12 ("T11598", 0.2, "secure e-voting"), ("T10764", 0.1, "privacy-preserving technologies")],13 subfields=[],14 arxiv=[("cs.CR", 0.2), ("cs.DC", 0.2), ("cs.GT", 0.1)],15 streams=[("conf/blockchain", 1.0), ("conf/icbc", 1.0), ("conf/aft", 1.0), ("conf/fc", 0.7), ("conf/bpm", 0.3),16 ("journals/jtaer", 0.2), ("journals/fi", 0.2), ("journals/ppna", 0.2), ("conf/ccs", 0.1), ("journals/tifs", 0.1),17 ("journals/tdsc", 0.1), ("journals/tnsm", 0.1), ("journals/tsc", 0.1), ("journals/tcss", 0.1), ("journals/compsec", 0.1),18 ("journals/itiis", 0.1), ("journals/iotj", 0.05), ("journals/tnse", 0.1), ("conf/trustcom", 0.2)],19 keywords=[20 (r"(?i)\bblock-?chains?\b|\bblockchain-?(based|enabled|powered)\b", 1.0, "both"),21 (r"(?i)\b(smart contracts?|distributed ledgers?|DLT\b|crypto-?currenc(y|ies)|Bitcoin|Ethereum|Solidity|EVM\b|Ethereum Virtual Machine|Hyperledger( Fabric)?|decentrali[sz]ed finance|DeFi\b|NFTs?\b|non-?fungible tokens?|proof[- ]of[- ](work|stake|authority|space|elapsed time|history|replication)|PoW\b|PoS\b|mining pools?|miners?|sharding (in|for|of) blockchain|layer[- ]2|rollups?|optimistic rollup|payment channels?|Lightning Network|cross-?chain|atomic swaps?|bridges? (between|across) (chains|blockchains)|decentrali[sz]ed (applications?|exchanges?|autonomous organi[sz]ations?|identity|storage networks?)|dApps?\b|DAOs?\b|Web3|tokenomics|token (economics|design)|MEV\b|maximal extractable value|front-?running|stablecoins?|central bank digital currenc|CBDCs?|zk-?rollups?|consensus (mechanisms?|protocols?|algorithms?) (for|in|of) (blockchain|permissioned|permissionless|ledgers?)|double-?spending|51% attacks?|selfish mining|transaction fees? (market|mechanism)|EIP-?1559|gas (fees?|optimi[sz]ation|costs?)|Merkle (trees?|proofs?) for ledgers?|UTXO|wallets?|private keys? management for|oracles? (problem|for blockchain|networks?)|Chainlink|IPFS|Filecoin|Solana|Cardano|Polkadot|Cosmos SDK|Tendermint|Avalanche consensus|Algorand|Libra|Diem|Monero|Zcash|privacy coins?)\b", 1.0, "both"),22 (r"(?i)\b(Byzantine (fault[- ]toleran(t|ce)|agreement|consensus|generals)|BFT\b|PBFT|HotStuff|Nakamoto consensus|longest-?chain|GHOST protocol|DAG-?based (consensus|ledgers?)|hashgraph|IOTA|Tangle)\b", 0.8, "both"),23 (r"(?i)\b(decentrali[sz]ed|decentrali[sz]ation|immutable (ledgers?|records?)|tamper-?proof|tamper-?evident|trustless|permissioned|permissionless|ledgers?|tokens?|crypto(-?assets?)?)\b", 0.4, "both"),24 ],25 exclusions=[r"(?is)^(?!.*(blockchain|ledger|smart contract|cryptocurrenc|Bitcoin|Ethereum|consensus|decentrali)).*\b(block chains? of|blocks? of (amino|residues|carbon|polymers?))\b"],26 related=["applied_cryptography", "distributed_computing", "cybersecurity", "privacy_preserving_computation", "computer_networks"],27 parent_of=[], child_of=[],28 provenance=("T10270 (168k works) is the OpenAlex blockchain topic and is sufficient alone; T13913 covers educational "29 "applications. BLOCKCHAIN/ICBC/AFT pure, FC 0.7; 'blockchain' is an unambiguous 1.0 keyword."),30),31dict(32 id="privacy_preserving_computation", number=36, name="Privacy-Preserving Computation", area="security_privacy",33 definition=("Computing on, releasing or sharing data while limiting what is learned about individuals: differential "34 "privacy, homomorphic encryption, secure multi-party computation and TEEs applied to data analysis and ML, "35 "anonymisation and synthetic data, private information retrieval, privacy attacks on models and their "36 "defences, machine unlearning, privacy engineering and regulation (GDPR). Federated learning is a child; "37 "cryptographic constructions as such are applied_cryptography."),38 topics=[("T10764", 1.0, "privacy-preserving technologies in data (federated learning, differential privacy, SMPC, k-anonymity)"),39 ("T11045", 0.6, "privacy, security and data protection"), ("T12943", 0.5, "COVID-19 digital contact tracing"),40 ("T11614", 0.4, "cloud data security (outsourced encrypted data)"), ("T10237", 0.3, "cryptography and data security"),41 ("T12921", 0.3, "European criminal justice and data protection"), ("T10927", 0.3, "access control and trust"),42 ("T11800", 0.2, "user authentication"), ("T13999", 0.2, "digital rights management"),43 ("T11693", 0.2, "cryptography and residue arithmetic (HE arithmetic)"), ("T11598", 0.2, "secure e-voting"),44 ("T10828", 0.2, "biometric identification (template protection)"), ("T11689", 0.2, "adversarial ML (membership inference)")],45 subfields=[],46 arxiv=[("cs.CR", 0.4), ("cs.LG", 0.1), ("cs.DB", 0.1), ("cs.CY", 0.1)],47 streams=[("journals/popets", 1.0), ("conf/pst", 0.6), ("journals/dud", 0.5), ("conf/satml", 0.4), ("conf/trustcom", 0.4),48 ("conf/ccs", 0.3), ("conf/uss", 0.3), ("conf/sp", 0.3), ("conf/ndss", 0.3), ("journals/tdsc", 0.3), ("conf/esorics", 0.3),49 ("conf/IEEEares", 0.3), ("journals/tifs", 0.2), ("journals/iacr", 0.2), ("journals/compsec", 0.2), ("journals/istr", 0.2),50 ("journals/ijisec", 0.2), ("conf/fat", 0.2), ("journals/tkde", 0.1), ("conf/sigmod", 0.1), ("journals/pvldb", 0.1),51 ("journals/cr", 0.2), ("conf/nips", 0.05), ("conf/icml", 0.05), ("journals/jamia", 0.1), ("journals/titb", 0.1)],52 keywords=[53 (r"(?i)\bprivacy-?preserving\b|\bprivacy-?enhancing\b|\bprivacy-?enhanced\b|\bPETs?\b|\bprivacy-?aware\b|\bprivacy-?friendly\b|\bprivacy-?protecting\b|\bprivacy-?first\b|\bprivate (computation|inference|training|learning|information retrieval|set intersection|aggregation|analytics|statistics|record linkage|data (analysis|release|sharing|publishing)|queries|counting|mean estimation|synthetic)\b", 1.0, "both"),54 (r"(?i)\b(differential(ly)?[- ]privac(y|ate)|DP-?SGD|DP-?FTRL|local differential privacy|LDP\b|central DP|shuffle (model|DP)|Rényi differential|Gaussian mechanism|Laplace mechanism|exponential mechanism|privacy (budget|amplification|accounting|accountant|loss|guarantees?|leakage|attacks?|risks?|threats?|auditing|audit|breaches?|violations?|harms?|threat models?|implications|preserving|protection|mechanisms?|filters?|definitions?)|\(ε|epsilon-?delta|ε-?differential|moments accountant)\b", 1.0, "both"),55 (r"(?i)\b(homomorphic encryption|fully homomorphic|somewhat homomorphic|FHE\b|HE-?based|CKKS|BFV\b|TFHE|SEAL library|secure multi-?party computation|secure (two|multi)-?party|MPC-?based|MPC protocols? for (machine learning|inference|training|data)|secret sharing(-| )based (computation|learning)|garbled circuits? for|oblivious (transfer|RAM|inference|shuffling)|ORAM|trusted execution environments? for (privacy|private)|SGX-?based (privacy|private)|private information retrieval|PIR\b|zero-?knowledge proofs? (for|of) (machine learning|inference|training|data)|zkML|k-?anonymity|l-?diversity|t-?closeness|anonymi[sz](ation|ed|ing)|pseudonymi[sz]|de-?anonymi[sz]ation|re-?identification (risks?|attacks?)|linkage attacks?|synthetic data (generation|release) (for|with) privacy|privacy-?preserving (machine learning|deep learning|data mining|data publishing|record linkage|federated|inference|training|analytics|blockchain|computation|aggregation|statistics|queries|authentication|recommendation|biometrics|smart grid|IoT|cloud|genomics)|PPML\b|PPDM\b|PPRL\b|membership inference|model inversion|attribute inference|property inference|gradient (leakage|inversion)|reconstruction attacks? (on|against) (models?|training data)|training data (extraction|leakage|memori[sz]ation)|data (leakage|minimi[sz]ation|protection by design)|machine unlearning|certified (unlearning|removal)|right to be forgotten|right to erasure|GDPR|CCPA|HIPAA (compliance|privacy)|data protection (regulation|laws?|impact assessment|authorities)|DPIA|consent management|cookie (consent|banners?)|privacy (polic(y|ies)|by design|engineering|compliance|regulation|laws?|labels?|nutrition labels?|dashboards?|settings|preferences|controls?)|purpose limitation|data (brokers?|sovereignty|trusts?)|personal data (processing|protection)|PII\b|personally identifiable)\b", 1.0, "both"),56 (r"(?i)\b(location privacy|trajectory privacy|genomic privacy|health data privacy|user privacy|data privacy|information privacy|individual privacy|privacy concerns?|privacy risks?|privacy (threat|attack) models?|privacy-?utility (trade-?off|tradeoff)|privacy (of|in|for) (users|individuals|patients|children|IoT|smart|mobile|location|voice|AR|VR|LLMs?|language models?|federated|blockchain|recommender|social)|inference attacks?|side-?channel (leakage|privacy)|website fingerprinting|traffic analysis attacks?|Tor\b|onion routing|anonymous (communication|credentials|networks?|messaging)|mix-?nets?|metadata privacy|unlinkability|unobservability|plausible deniability|contact tracing (apps?|privacy|protocols?)|DP-?3T|exposure notification)\b", 0.8, "both"),57 (r"(?i)\bprivacy\b|\bprivate\b|\bconfidential(ity)?\b|\banonymous\b|\bsensitive (data|attributes?|information)\b", 0.4, "both"),58 ],59 exclusions=[r"(?is)^(?!.*(comput|data|algorithm|protocol|model|system|encrypt|differential|anonym|learning|inference|database|app|online|digital|users?)).*\b(privacy (in|of) (nursing|hospital rooms?|the home|toilets?|bedrooms?|bathrooms?)|private (practice|sector|hospitals?|schools?|equity|insurance|property|lands?|forests?|firms?|companies|banks?|clinics?|households?))\b"],60 related=["applied_cryptography", "cybersecurity", "ai_safety", "database_systems", "blockchain", "federated_learning", "ml_theory"],61 parent_of=["federated_learning"], child_of=[],62 provenance=("T10764 (97k works) keywords list federated learning, differential privacy, SMPC, k-anonymity: it is the host of both "63 "this category and its child. PoPETs is the only pure venue; security venues partial. Non-computing senses of "64 "'private/privacy' are vetoed when no computing term co-occurs."),65),66dict(67 id="federated_learning", number=37, name="Federated Learning", area="security_privacy",68 definition=("Training models across multiple clients or institutions without centralising their raw data: federated "69 "averaging and optimisation under heterogeneity, communication efficiency, client selection, personalisation, "70 "secure aggregation and privacy of federated systems, robustness to malicious clients, vertical and "71 "cross-silo federated learning, federated fine-tuning of foundation models, decentralised/gossip learning. "72 "A child of privacy_preserving_computation; also linked to distributed_computing and edge_computing."),73 topics=[("T10764", 0.7, "privacy-preserving technologies (federated learning, non-IID data, communication efficiency)"),74 ("T10273", 0.1, "IoT and edge computing (federated learning at the edge)"),75 ("T11307", 0.1, "domain adaptation / personalisation"), ("T11689", 0.1, "adversarial robustness (Byzantine clients)")],76 subfields=[],77 arxiv=[("cs.LG", 0.2), ("cs.DC", 0.2), ("cs.CR", 0.1), ("cs.NI", 0.1)],78 streams=[("journals/tmc", 0.1), ("journals/iotj", 0.1), ("journals/tpds", 0.1), ("journals/tifs", 0.05), ("conf/satml", 0.1),79 ("journals/tnse", 0.1), ("journals/tnsm", 0.05), ("journals/twc", 0.05), ("journals/jsac", 0.05), ("conf/infocom", 0.05),80 ("journals/tnn", 0.05), ("journals/tcc", 0.05), ("conf/nips", 0.02), ("conf/icml", 0.02), ("conf/iclr", 0.02)],81 keywords=[82 (r"(?i)\bfederated (learning|optimi[sz]ation|training|averaging|analytics|transfer learning|reinforcement learning|graph (learning|neural)|fine-?tuning|LLMs?|foundation models?|unlearning|recommendation|recommender|distillation|clustering|evaluation|inference|systems?|settings?|frameworks?|clients?|servers?|rounds?|aggregation|edge|IoT|healthcare|medical|split|meta-?learning|continual|domain|representation|contrastive|self-?supervised|semi-?supervised|prompt|instruction|benchmarks?|datasets?|simulations?|deployments?|platforms?)\b", 1.0, "both"),83 (r"\bFL\b", 0.5, "both"),84 (r"(?i)\b(FedAvg|FedProx|FedSGD|SCAFFOLD|FedNova|FedOpt|FedAdam|FedAdagrad|FedYogi|FedBN|FedPer|FedProto|FedDyn|FedMA|MOON\b|Ditto|pFedMe|FedRep|FedALA|FedLoRA|FedIT|FedBERT|personali[sz]ed federated|cross-?silo|cross-?device|non-?IID (data|clients?|settings?|distributions?)|statistical heterogeneity|client (drift|heterogeneity|selection|sampling|participation|dropout|unavailability|stragglers?)|secure aggregation|SecAgg|split learning|SplitFed|decentrali[sz]ed (learning|training|SGD|optimi[sz]ation|federated)|gossip (learning|SGD|averaging)|collaborative learning (without|across) (sharing|institutions?|data silos?|hospitals?)|swarm learning|vertical federated|horizontal federated|VFL\b|HFL\b|federated-?learning|Flower framework|FATE\b|FedML|TensorFlow Federated|TFF\b|LEAF benchmark|FedScale|model poisoning in|Byzantine-?robust (aggregation|federated)|Krum|Trimmed mean|free-?riders? in federated|incentive mechanisms? for federated|federated (unlearning|forgetting))\b", 1.0, "both"),85 (r"(?i)\b(communication-?efficient (distributed|learning|training|federated)|gradient (compression|sparsification|quanti[sz]ation) (for|in) (distributed|federated)|model aggregation|aggregation server|local (updates?|epochs?|training|steps?) (on|at) (clients?|devices?|the edge)|global model (and|is) (local|aggregated)|parameter server (for|and) (clients|devices)|on-?device (training|learning) (without|with) (sharing|privacy))\b", 0.6, "both"),86 (r"(?i)\b(privacy-?preserving (distributed|collaborative|decentrali[sz]ed) (learning|training|machine learning|deep learning)|data silos?|clients?|edge devices? (train|collaborat)|without (sharing|centrali[sz]ing|uploading|exchanging) (raw |local |private |their )?data)\b", 0.6, "both"),87 ],88 exclusions=[r"(?is)^(?!.*(learning|training|model|client|aggregat|distributed|privacy)).*\b(federated (states?|republic|search|identity|database|cloud|governance|architecture|systems? of|network|information systems?|authentication|SSO|single sign)|federation of)\b"],89 related=["privacy_preserving_computation", "distributed_computing", "edge_computing", "ml_theory", "ai_safety", "foundation_models"],90 parent_of=[], child_of=["privacy_preserving_computation"],91 provenance=("No OpenAlex topic or DBLP stream isolates federated learning (it lives inside T10764 and in ML/networking venues), "92 "so 'federated <learning-term>' patterns and algorithm names (FedAvg, FedProx, SCAFFOLD...) carry the category at 1.0; "93 "bare 'FL' is 0.5 and case-sensitive. Non-ML senses of 'federated' are vetoed when no learning term co-occurs."),94),95]96