01925nam a2200289 a 450000100080000000500110000800800410001902200140006010000260007424501030010026000550020330000100025849000850026852008760035365300260122965300290125565300160128465300250130065300570132565300740138265300470145665300350150365300310153865300220156965300240159165300200161510048852022-09-06       2006    bl uuuu     u0uu1 u    #d  a1677-92661 aOLIVEIRA, S. R. de M.  aHeuristics for protecting competitive knowledge in association rule mining.h[electronic resource]  aCampinas: Embrapa Informática Agropecuáriac2006  a48 p.  a(Embrapa Informática Agropecuária. Boletim de pesquisa e desenvolvimento, 13).  aThe sharing of data for mining has been proven beneficial in industry, but requires privacy safeguards. Some companies prefer to share their data for collaboration, while others decide to share only the patterns discovered from their data. The goal of these companies is to disclose only part of the knowledge and conceal a group of sensitive rules (competitive knowledge) that are paramount for strategic decisions. These rules must be protected before sharing and need to remain private. The challenge here is how to protect the sensitive rules without putting at risk the effectiveness of data mining per se. This work presents a framework for protecting sensitive knowledge in Association Rule Mining. The framework is composed of a set of heuristics and metrics to evaluate the effectiveness of these heuristics in terms of information loss and knowledge protection.  aCompetitive knowledge  aConhecimento competitivo  aHeurística  aKnowledge protection  aPreservação de privacidade em mineração de dados  aPreservação de privacidade em mineração de regras de associação  aPrivacy-preserving association rule mining  aPrivacy-preserving data mining  aProteção de conhecimento  aRegras sensíveis  aSensitive knowledge  aSensitive rules