Privacy-friendly forecasting for the smart grid using homomorphic encryption and the group method of data handling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10239 LNCS, Issue , 2017, Pages 184-201

  Bos, Joppe W ^a   Castryck, Wouter ^b,c   Iliashenko, Ilia ^b   Vercauteren, Frederik ^b,d  

^a NXP SEMICONDUCTORS   (Netherlands)

^b Katholieke Universiteit Leuven   (Belgium)

^c UNIVERSITÉ LILLE 1   (France)

^d Open Security Research   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DATA HANDLING; DATA PRIVACY; ELECTRIC POWER TRANSMISSION NETWORKS; EXPERIMENTAL REACTORS; FORECASTING; NEURAL NETWORKS; SMART POWER GRIDS;

ACTIVATION FUNCTIONS; GROUP METHOD OF DATA HANDLING; HO-MOMORPHIC ENCRYPTIONS; MEAN ABSOLUTE PERCENTAGE ERROR; PARALLEL IMPLEMENTATIONS; PREDICTION ALGORITHMS; SEQUENTIAL IMPLEMENTATION; SOMEWHAT HOMOMORPHIC ENCRYPTIONS;

CRYPTOGRAPHY;

EID: 85019545969     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-57339-7_11     Document Type: Conference Paper

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