PROTÓTIPOS DE CARRINHOS ELÉTRICOS MOVIDOS A ENERGIA SOLAR

O presente trabalho visa demonstrar a experiência de projeto e montagem de protótipos de carros elétricos movidos a energia solar/luminosa. A experiência se desenvolveu durante as aulas do Projeto Integrador III, do módulo III, do Curso de PROEJA em Eletromecânica

Privacy-awareness of distributed data clustering algorithms revisited

Several privacy measures have been proposed in the privacy preserving data mining literature. However, privacy measures either assume centralized data source or that no insider is going to try to infer some information. This paper presents distributed privacy measures that take into account collusion attacks and point level breaches for distributed data clustering. An analysis of representative distributed data clustering algorithms show that collusion is an important source of privacy issues and that the analyzed algorithms exhibit different vulnerabilities to collusion groups

Distributed Data Mining and Agents

Abstract. Multi-Agent Systems (MAS) offer an architecture for distributed problem solving. Distributed Data Mining (DDM) algorithms focus on one class of such distributed problem solving tasks—analysis and modeling of distributed data. This paper offers a perspective on DDM algorithms in the context of multiagents systems. It discusses broadly the connection between DDM and MAS. It provides a high-level survey of DDM, then focuses on distributed clustering algorithms and some potential applications in multi-agent-based problem solving scenarios. It reviews algorithms for distributed clustering, including privacypreserving ones. It describes challenges for clustering in sensor-network environments, potential shortcomings of the current algorithms, and future work accordingly. It also discusses confidentiality (privacy preservation) and presents a new algorithm for privacy-preserving density-based clustering