Optimization of privacy-aware cloud crowdsourcing resource combinations for product development.

Crowdsourcing offers the prospect of a more open and socialized approach to product development by bringing together crowdsourcing community resources and connecting open innovation participants. Doing this effectively involves finding an optimal combination of crowdsourcing resources. However, the crowdsourcing community typically focuses on realizing a wide variety of different micro-tasks to meet overall customer requirements, giving rise to numerous potential ways of combining service resources. The parties making up different crowdsourcing community platforms also have various kinds of privacy preferences, making it hard for operators to disclose all of the information relevant to the development of a particular product. This paper proposes a privacy-aware strategy for the optimal combination of crowdsourcing product development resources based on edge computing and cloud computing. Product development application scenarios are examined that envisage the deployment of privacy-aware edge computing and collaboration between a central cloud and edge-based crowdsourcing communities. The crowdsourcing community environment and its privacy risk model are analyzed and the construction and presentation of cloud crowdsourcing platforms for complex tasks are studied. A mechanism for uncovering the optimal cross-platform combination of crowdsourcing product development services based on privacy-aware cloud-edge collaboration is then elaborated, together with a technique for implementing the proposed mechanism via a cooperative bacteria foraging optimization (CBFO) algorithm. Random test data was obtained based on a cloud crowdsourcing platform and two sets of validation tests were undertaken that show that the proposed method can preserve the privacy of crowdsourcing communities, while efficiently solving the problem of the cross-platform mobilization of crowdsourced product development resources. The approach is compared with more basic bacteria foraging optimization (BFO) and genetic algorithm (GA)-based techniques and it is found that CBFO offers better computational performance when confronted with convergence and other factors that can influence the resource combination optimization model, such as a change in the number of micro-tasks. [ABSTRACT FROM AUTHOR]