Abstract
The ever-increasing popularity of mobile devices (e.g., mobile phones and smart watches) has created a variety of crowdsourcing applications by employing the massive and distributed mobile computing resources. Typically, a task requester sends his/her task request and constraint conditions to a crowdsourcing platform, and then the crowdsourcing platform is responsible for finding a set of appropriate workers (e.g., mobile users) from massive candidates to satisfy the task request. However, for a mobile crowdsourcing task being executed by a set of workers, a pre-selected worker may become unavailable due to various exceptions. In this situation, it is significant for the crowdsourcing platform to quickly find another similar worker to replace the unavailable worker so as to smooth the crowdsourcing process. However, the above exception handling process is often challenging as candidate workers are often not willing to release their sensitive information to the platform due to privacy concerns. In view of this challenge, in this paper, a novel privacy-preserving exception handling approach, named ExHSimhash, is put forward based on Simhash technique. Finally, through a set of simulated experiments, we validate the feasibility of ExHSimhash in terms of substitution equivalence and computational time.
Highlights
With the ever-increasing popularity of mobile computing techniques in daily life, people are apt to execute their business applications or complete their computing tasks through various mobile devices, such as mobile phones, smart watches, and laptops [1–8]
The privacy-preserving exception handling problem can be specified as below: if a previously selected worker workerexcep becomes unavailable due to some exceptions, the crowdsourcing platform CP should quickly find another candidate worker workerx who is similar with workerexcep, and guarantee that the sensitive information contained in vectors Vi (1 ≤ i ≤ m) is still secure
4 Exception handling for mobile crowdsourcing based on Simhash we introduce the details of our suggested exception handling approach for mobile crowdsourcing, i.e., ExHSimhash
Summary
With the ever-increasing popularity of mobile computing techniques in daily life, people are apt to execute their business applications or complete their computing tasks through various mobile devices, such as mobile phones, smart watches, and laptops [1–8]. The privacy-preserving exception handling problem can be specified as below: if a previously selected worker workerexcep becomes unavailable due to some exceptions, the crowdsourcing platform CP should quickly find another candidate worker workerx who is similar with workerexcep, and guarantee that the sensitive information contained in vectors Vi (1 ≤ i ≤ m) is still secure. Workeroptimal 1⁄4 fworkeÀrx jÀworkerÁx∈WORKÁERX and d H V excep ; HðV xÞ is the minimalg ð6Þ workers u and v are similar, we can conclude that v is an ideal alternative for u when u becomes unavailable Motivated by this hypothesis, a new criterion “substitution equivalence” (∈[0, 1], the larger the better) is defined as in (7), where A and B denote the set of tasks executed by workeroptimal and workerexcep, respectively (Fig. 2). The exception handling efficiency of our approach performs better than the other three approaches, which means that ExHSimhash is suitable for the mobile crowdsourcing situations when a quick exception handling response is required by the task requester
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