GUARanteeD confidentiality and efficiency in CROWDsourcing platforms – CROWDGUARD

Crowdsourcing platforms offer the unprecedented opportunity to connect easily on-demand task providers, or taskers, and on-demand task solvers, or workers, locally or world-wide, for paid or voluntary work, and for various kinds of tasks. By facilitating the accurate search of specific workers, otherwise unavailable, they have the potential to reduce costs as well as to accelerate and even democratize innovation. Their growing importance has made them unavoidable actors of the 21st century economy. However, abusive behaviors from crowdsourcing platforms against taskers or workers are frequently reported in the news or on dedicated websites, whether performed willingly or not, putting them at the epicenter of a burning societal debate. Real-life examples of such abusive behaviors range from strong concerns about private information accesses and uses (see, e.g., the privacy scandals due to illegitimate accesses to the location data of a well-known drivers-riders company – tinyurl.com/wp-priv) to blatant denials of workers' independence (see, e.g., the complaints of micro-task workers or of drivers about the strong work control and monitoring imposed by their respective platforms – tinyurl.com/wsj-ind and tinyurl.com/trans-ind). This fuels the growing concern of individuals, overshadowing the possible benefits that crowdsourcing processes can bring to societies. In addition to obvious legal and ethical reasons, protecting both taskers and workers – i.e., the two sides of a crowdsourcing platform – from the platform itself, is thus crucial for establishing sound trust foundations.

The goal of the CROWDGUARD project is to design sound protection measures of the taskers and workers from threats coming from the platform, while still enabling the latter to perform efficient and accurate tasks assignments. In CROWDGUARD, we advocate for an approach that uses confidentiality and privacy guarantees as building blocks for preventing a large variety of abusive behaviors. First, the enforcement of privacy and confidentiality guarantees directly prevents the first kind of abuse that we consider, i.e., the abusive usage of the personal or confidential information that taskers and workers disclose to the platform for the assignment of tasks. Second, through their obfuscation abilities, privacy and confidentiality guarantees carry the promise, in an extended form, to be also efficient for preventing a large variety of abusive behaviors (e.g, non-discrimination, or workers' independence).

The CROWDGUARD project will specify relevant use-cases, extracted from real-life situations and illustrating the need to protect the crowd from various abusive behaviors from the platform. The project will propose secure distributed algorithms for allowing workers (resp. taskers) to collaboratively compute a privacy-preserving version of their profiles (resp. a confidentiality-preserving version of their tasks) which will then be sent to the platform. The resulting tasks and profiles will enable highly efficient and accurate crowdsourcing processes while being protected by sound confidentiality and privacy guarantees. CROWDGUARD will also identify and formalize the possible abusive behaviors that the platform may perform, and propose sound models/algorithms to prevent them. Finally, the project will develop a prototype that will be used for evaluating the efficiency of the techniques proposed.

The main scientific outcomes of CROWDGUARD will advance the state-of-the-art on sound models and algorithms for the definition and prevention of abusive behaviors from crowdsourcing platforms. They will enable the development of respectful crowdsourcing processes by private companies or associations.