skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 5:00 PM ET until 11:00 PM ET on Friday, June 21 due to maintenance. We apologize for the inconvenience.


Title: Accurate inference of crowdsourcing properties when using efficient allocation strategies
Abstract

Allocation strategies improve the efficiency of crowdsourcing by decreasing the work needed to complete individual tasks accurately. However, these algorithms introduce bias by preferentially allocating workers onto easy tasks, leading to sets of completed tasks that are no longer representative of all tasks. This bias challenges inference of problem-wide properties such as typical task difficulty or crowd properties such as worker completion times, important information that goes beyond the crowd responses themselves. Here we study inference about problem properties when using an allocation algorithm to improve crowd efficiency. We introduce Decision-Explicit Probability Sampling (DEPS), a novel method to perform inference of problem properties while accounting for the potential bias introduced by an allocation strategy. Experiments on real and synthetic crowdsourcing data show that DEPS outperforms baseline inference methods while still leveraging the efficiency gains of the allocation method. The ability to perform accurate inference of general properties when using non-representative data allows crowdsourcers to extract more knowledge out of a given crowdsourced dataset.

 
more » « less
NSF-PAR ID:
10366710
Author(s) / Creator(s):
;
Publisher / Repository:
Nature Publishing Group
Date Published:
Journal Name:
Scientific Reports
Volume:
12
Issue:
1
ISSN:
2045-2322
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Mobile and web apps are increasingly relying on the data generated or provided by users such as from their uploaded documents and images. Unfortunately, those apps may raise significant user privacy concerns. Specifically, to train or adapt their models for accurately processing huge amounts of data continuously collected from millions of app users, app or service providers have widely adopted the approach of crowdsourcing for recruiting crowd workers to manually annotate or transcribe the sampled ever-changing user data. However, when users' data are uploaded through apps and then become widely accessible to hundreds of thousands of anonymous crowd workers, many human-in-the-loop related privacy questions arise concerning both the app user community and the crowd worker community. In this paper, we propose to investigate the privacy risks brought by this significant trend of large-scale crowd-powered processing of app users' data generated in their daily activities. We consider the representative case of receipt scanning apps that have millions of users, and focus on the corresponding receipt transcription tasks that appear popularly on crowdsourcing platforms. We design and conduct an app user survey study (n=108) to explore how app users perceive privacy in the context of using receipt scanning apps. We also design and conduct a crowd worker survey study (n=102) to explore crowd workers' experiences on receipt and other types of transcription tasks as well as their attitudes towards such tasks. Overall, we found that most app users and crowd workers expressed strong concerns about the potential privacy risks to receipt owners, and they also had a very high level of agreement with the need for protecting receipt owners' privacy. Our work provides insights on app users' potential privacy risks in crowdsourcing, and highlights the need and challenges for protecting third party users' privacy on crowdsourcing platforms. We have responsibly disclosed our findings to the related crowdsourcing platform and app providers.

     
    more » « less
  2. Spatial crowdsourcing (SC) enables task owners (TOs) to outsource spatial-related tasks to a SC-server who engages mobile users in collecting sensing data at some specified locations with their mobile devices. Data aggregation, as a specific SC task, has drawn much attention in mining the potential value of the massive spatial crowdsensing data. However, the release of SC tasks and the execution of data aggregation may pose considerable threats to the privacy of TOs and mobile users, respectively. Besides, it is nontrivial for the SC-server to allocate numerous tasks efficiently and accurately to qualified mobile users, as the SC-server has no knowledge about the entire geographical user distribution. To tackle these issues, in this paper, we introduce a fog-assisted SC architecture, in which many fog nodes deployed in different regions can assist the SC-server to distribute tasks and aggregate data in a privacy-aware manner. Specifically, a privacy-aware task allocation and data aggregation scheme (PTAA) is proposed leveraging bilinear pairing and homomorphic encryption. PTAA supports representative aggregate statistics (e.g.,sum, mean, variance, and minimum) with efficient data update while providing strong privacy protection. Security analysis shows that PTAA can achieve the desirable security goals. Extensive experiments also demonstrate its feasibility and efficiency. 
    more » « less
  3. null (Ed.)
    Abstract Collaborative work often benefits from having teams or organizations with heterogeneous members. In this paper, we present a method to form such diverse teams from people arriving sequentially over time. We define a monotone submodular objective function that combines the diversity and quality of a team and proposes an algorithm to maximize the objective while satisfying multiple constraints. This allows us to balance both how diverse the team is and how well it can perform the task at hand. Using crowd experiments, we show that, in practice, the algorithm leads to large gains in team diversity. Using simulations, we show how to quantify the additional cost of forming diverse teams and how to address the problem of simultaneously maximizing diversity for several attributes (e.g., country of origin and gender). Our method has applications in collaborative work ranging from team formation, the assignment of workers to teams in crowdsourcing, and reviewer allocation to journal papers arriving sequentially. Our code is publicly accessible for further research. 
    more » « less
  4. Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average. 
    more » « less
  5. With the rapid growth of online social media and ubiquitous Internet connectivity, social sensing has emerged as a new crowdsourcing application paradigm of collecting observations (often called claims) about the physical environment from humans or devices on their behalf. A fundamental problem in social sensing applications lies in effectively ascertaining the correctness of claims and the reliability of data sources without knowing either of them a priori, which is referred to as truth discovery. While significant progress has been made to solve the truth discovery problem, some important challenges have not been well addressed yet. First, existing truth discovery solutions did not fully solve the dynamic truth discovery problem where the ground truth of claims changes over time. Second, many current solutions are not scalable to large-scale social sensing events because of the centralized nature of their truth discovery algorithms. Third, the heterogeneity and unpredictability of the social sensing data traffic pose additional challenges to the resource allocation and system responsiveness. In this paper, we developed a Scalable Streaming Truth Discovery (SSTD) solution to address the above challenges. In particular, we first developed a dynamic truth discovery scheme based on Hidden Markov Models (HMM) to effectively infer the evolving truth of reported claims. We further developed a distributed framework to imple- ment the dynamic truth discovery scheme using Work Queue in HTCondor system. We also integrated the SSTD scheme with an optimal workload allocation mechanism to dynamically allocate the resources (e.g., cores, memories) to the truth discovery tasks based on their computation requirements. We evaluated SSTD through real world social sensing applications using Twitter data feeds. The evaluation results on three real-world data traces (i.e., Boston Bombing, Paris Shooting and College Football) show that the SSTD scheme is scalable and outperforms the state-of-the- art truth discovery methods in terms of both effectiveness and efficiency. 
    more » « less