{"id":"W1544588077","doi":"10.1007/978-3-642-14785-2_12","title":"Data Aggregation Integrity Based on Homomorphic Primitives in Sensor Networks","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Security in Wireless Sensor Networks","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Homomorphic encryption; Base station; Computer network; Node (physics); Wireless sensor network; Key (lock); Payload (computing); Scheme (mathematics); Message authentication code; Data aggregator; Public-key cryptography; Hash function; Computation; Aggregate (composite); Data integrity; Distributed computing; Encryption; Computer security; Cryptography; Network packet; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002297238,0.0005731753,0.001109592,0.0007575639,0.0008679135,0.002726569,0.00149081,0.0009669173,0.001880772],"category_scores_gemma":[0.00438034,0.0007192841,0.0005906589,0.001364884,0.002778531,0.006659698,0.002857344,0.003500839,0.0005500548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007802611,"about_ca_system_score_gemma":0.0008433668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001913515,"about_ca_topic_score_gemma":0.0001337136,"domain_scores_codex":[0.9971557,0.0007807443,0.0002033539,0.0003301894,0.00133935,0.0001907275],"domain_scores_gemma":[0.9955746,0.00158202,0.0002358113,0.002163017,0.0003717021,0.0000728853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003361717,0.0001092799,0.0004786745,0.0002528403,0.00004888915,0.0002334242,0.0004930187,0.0269625,0.01162616,0.7913275,0.004904538,0.1632271],"study_design_scores_gemma":[0.00005896372,0.0001503919,0.0003639415,0.00007313526,0.00005031064,0.0006486811,0.0001225005,0.2348828,0.03004559,0.7187354,0.01482305,0.00004524998],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03254516,0.002012934,0.9488831,0.0006251755,0.0002336774,0.00009791858,0.00008019955,0.001090839,0.01443096],"genre_scores_gemma":[0.7487788,0.00263076,0.2342638,0.000255268,0.0004254217,0.0002346451,0.0002435023,0.000354113,0.01281378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002726569,"threshold_uncertainty_score":0.0121491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03315726555328669,"score_gpt":0.2631973923589797,"score_spread":0.230040126805693,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}