Trust, Security, and Privacy in Crowdsourcing

Guest Editorial to Special Issue of IEEE Internet of Things Journal: “As we become increasingly reliant on intelligent, interconnected devices in every aspect of our lives, critical trust, security, and privacy concerns are raised as well.

First, the sensing data provided by individual participants is not always reliable. It may be noisy or even faked due to various reasons, such as poor sensor quality, lack of sensor calibration, background noise, context impact, mobility, incomplete view of observations, or malicious attacks. The crowdsourcing applications should be able to evaluate the trustworthiness of collected data in order to filter out the noisy and fake data that may disturb or intrude a crowdsourcing system. Second, providing data (e.g., photographs taken with personal mobile devices) or using IoT applications may compromise data providers’ personal data privacy (e.g., location, trajectory, and activity privacy) and identity privacy. Therefore, it becomes essential to assess the trust of the data while preserving the data providers’ privacy. Third, data analytics and mining in crowdsourcing may disclose the privacy of data providers or related entities to unauthorized parities, which lowers the willingness of participants to contribute to the crowdsourcing system, impacts system acceptance, and greatly impedes its further development. Fourth, the identities of data providers could be forged by malicious attackers to intrude the whole crowdsourcing system. In this context, trust, security, and privacy start to attract a special attention in order to achieve high quality of service in each step of crowdsourcing with regard to data collection, transmission, selection, processing, analysis and mining, as well as utilization.

Trust, security, and privacy in crowdsourcing receives increasing attention. Many methods have been proposed to protect privacy in the process of data collection and processing. For example, data perturbation can be adopted to hide the real data values during data collection. When preprocessing the collected data, data anonymization (e.g., k-anonymization) and fusion can be applied to break the links between the data and their sources/providers. In application layer, anonymity is used to mask the real identities of data sources/providers. To enable privacy-preserving data mining, secure multiparty computation (SMC) and homomorphic encryption provide options for protecting raw data when multiple parties jointly run a data mining algorithm. Through cryptographic techniques, no party knows anything else than its own input and expected results. For data truth discovery, applicable solutions include correlation-based data quality analysis and trust evaluation of data sources. But current solutions are still imperfect, incomprehensive, and inefficient….(More)”.