Continuous Probabilistic Skyline Query for Secure Worker Selection in Mobile Crowdsensing

Truth based three-tier Combinatorial Multi-Armed Bandit ecosystems for mobile crowdsensing

Mobile Crowdsensing (MCS) is a new paradigm that leverages the collective sensing ability of a crowd so that a special task can be performed through the aggregation of information collected from personal mobile devices. While MCS brings several benefits, its application is prevented by challenges such as the efficient recruitment of users, effective mechanisms for rewarding users to encourage participation, and an effective and fast enough approach for managing the underlying resources that support large-scale MCS applications involving a large number of people in data collection. On the other hand, Artificial Intelligence (AI) applications, which are mostly based on Deep Neural Networks (DNN), are becoming pervasive today and are executed by the end users’ mobile devices, which are characterised by limited memory and computing power, and low battery level. This paper describes and evaluates an incentive mechanism for a mobile crowdsensing system with an AI sensing task based on a one-leader multi-follower Stackelberg game. The MCS platform, as a leader, provides an AI sensing task to be executed by a DNN, which can be deployed in two different ways: fully on the user device or partially on the device and partially on edge or cloud resources. The users, as followers, make their decisions regarding their participation to the MCS system and select their desired deployment given the energy and memory available on their device and the deployment reward proposed by the MCS platform. The goals of the MCS platform are: i) to motivate the users to participate in the system, ii) to maximize its profit, and iii) to identify the optimal resources supporting the sensing task that minimizes the cost and provide performance guarantees. This problem has been formulated as a mixed integer nonlinear program and propose an efficient algorithmic approach to solve it quickly. The proposed approach has been compared with some baseline methods and with BARON state-of-the-art solver. Results show that our approach converges to the optimal solution much faster than BARON (up to orders of magnitude) especially in large scale systems. Furthermore, the comparison to the baseline methods shows that our approach always beats the best baseline method under different scenarios providing up to 16% improvement for the MCS platform profit.

Mobile crowdsensing (MCS) has heated up and has become a new paradigm of data collection. In the process of the task allocation of MCS, users are often required to provide their own location information with the server to conveniently dispatch some suitable tasks to them. However, it is possible for malicious servers to infer some sensitive information based on the user’s location such as the user’s home address or the user’s trajectory and so on which will cause serious privacy issues. Recently, differential privacy (DP) has become a promising privacy protection scheme. However, the existing DP schemes in location privacy protection for MCS do not pay attention to the accuracy of task allocation as equally as the effects of privacy protection, which often results in the task allocation to be inaccurate and inefficient. In order to overcome the shortcoming, we propose a novel MCS task allocation scheme integrating the mapping accuracy of task-worker with the privacy-preserving effect on the user’s location. Besides edge devices, its implementation system consists of a task allocation server and a third party, both of which are semitrusted, that is, both of them only know about the user’s rough location information but cannot obtain the user’s exact location. At first, to improve the response speed of MCS task requests and the location privacy protection of MCS users, our scheme can exploit more than one edge-computing node nearby a user to cooperatively participate in MCS task allocation by aggregating him/her and the near users into groups. Then, our scheme can further enhance the location privacy protection effect based on the Johnson–Lindenstrauss (JL) transformation, which can achieve accurate task allocation and hold the merits of DP. Finally, we verify the feasibility by some experiments based on two data sets. The performance is compared with that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> he typical DP. The results show that our scheme not only provides strict privacy guarantees but also has higher performance.

With the pervasiveness of mobile devices, satisfying spatial-temporal coverage requirements in interested regions while considering the quality of the sensing data and the budget constraint is a major research challenge in mobile crowdsensing (MCS). In this paper, we define a novel coverage metric, quality coverage, which considers both the fraction of subareas covered by sensor readings and the quality of sensing data in each covered subarea, for diversified location-based MCS tasks. In our system, as participants with high reliability level can contribute a lot to sensing tasks, we design an incentive model that provides higher bonus to more reliable participants. After knowing the reward for the per amount of sensing data, each worker can submit an interested task set that contains several unit tasks. The platform assigns MCS tasks to these mobile workers by solving the task allocation optimization problems, which select the optimal mobile worker set under the budget of data requesters to maximize diverse and spatial coverage level in the whole sensing area. As the task allocation problems are proven to be NP-hard, we propose a heuristic and a polynomialtime greedy approximation algorithm to solve optimization problems in two different application scenarios. Simulations using both real and synthetic data sets show that our algorithms outperform existing approaches.

As one of the most fundamental problems in mobile crowdsensing (MCS), worker selection has drawn significant attention in recent years. However, very few studies consider the workers&#x0027; spatio- and temporal-coverage for the sensing task. In this paper, we propose a novel top-k worker selection scheme such that the MCS platform can select qualified workers in terms of spatio-temporal similarity. Besides, we design a novel privacy-preserving approach for protecting participants&#x0027; spatio-temporal information based on the modified Paillier encryption technique. Detailed security analysis showed that the task re-questor&#x0027;s temporal information and the workers&#x0027; spatio-temporal information are preserved and will not be revealed to any other parties. Extensive experiments are conducted, and the results demonstrate that our scheme outperforms the baseline methods regarding the selection of reliable workers.

Mobile crowdsensing (MCS) depends on mobile users to collect sensing data, whose quality highly depends on the expertise/experience of the users. It is critical for MCS to identify right persons for a given sensing task. A commonly-used strategy is to “teach-before-use”, i.e., training users with a set of questions and selecting a subset of users who have answered the questions correctly the most of times. This method has large room for improvement if we consider users’ learning curve during the training process. As such, we propose an interactive learning pattern recognition framework, Goldilocks, that can filter users based on their learning patterns. Goldilocks uses an adaptive teaching method tailored for each user to maximize her learning performance. At the same time, the teaching process is also the selecting process. A user can thus be safely excluded as early as possible from the MCS tasks later on if her performance still does not match the desired learning pattern after the training period. Experiments on real-world datasets show that compared to the baseline methods, Goldilocks can identify suitable users to obtain more accurate and more stable results for multi-categories classification problems.

Although mobile crowdsensing (MCS) has become a new paradigm of collecting, analyzing, and exploiting massive amounts of sensory data, how to effectively incentivize users’ active participation and high quality data contribution in MCS has become a critical issue. Existing offline incentive mechanisms are inefficient to be applied to the online scenario, where a large number of heterogeneous users arrive and leave dynamically in a random manner, meanwhile the MCS platform has to make an irrevocable immediate decision on whether to accept or reject users’ requests, and how much a user should be paid under a strict budget constraint without knowing future information. Although there are a few online incentive mechanisms, these efforts tend to focus on achieving some desirable properties without addressing the issues of online incentive as a whole, and are not effective to motivate users’ active participation and high-quality sensory data contribution simultaneously. In this paper, we integrate the quality of sensing, rating update, user selection, and payment determination to develop the first online rating protocol for practical MCS to deal with adverse selection and moral hazard simultaneously. First, we design an endogenous learning approach to quantify the users’ ratings based on their historical contributions. Second, we exploit an incremental learning approach to compute the density threshold based on users’ historical information. Third, we theoretically prove that our online rating protocol achieves the desirable properties of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">computationally efficiency</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">budgetary feasibility</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">individual rationality</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">truthfulness</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">constant competitiveness</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rating coverage</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incentive effectiveness</i> . Finally, extensive evaluation results support the performance of our protocol and validate the theoretical properties derived in this paper.

Mobile crowdsensing (MCS) is a ubiquitous sensing paradigm that emerged in the form of”sensed data as a service” model in the Internet of Things Era. Distributed nature of MCS results in vulnerabilities at the MCS platforms as well as participating devices that provide sensory data services. Submission of fake tasks with the aim of clogging sensing server resources and draining participating device batteries is a crucial threat that has not been investigated well. In this paper, we provide a detailed analysis by modeling a deep belief network (DBN) when the available sensory data is scarce for analysis. With oversampling to cope with the class imbalance challenge, a Principal Component Analysis (PCA) module is implemented prior to the DBN and weights of various features of sensing tasks are analyzed under varying inputs. The experimental results show that the presented DBN-driven fake task mitigation detection of fake sensing tasks can ensure up to 0.92 accuracy, 0.943 precision and up to 0.928 F1 score outperforming prior work on MCS data with deep learning networks.

Mobile crowdsensing (MCS) can be widely adopted for undertaking a variety of tasks for the smart city by recruiting distributed participants. With the rapid proliferation of MCS applications, tasks can be devised with different sensing scales. Hence, allocating the tasks to participants with variable spatiotemporal granularity would be urgent and challenging. To this end, we propose a behavior sequentializing and task mapping (BETA) framework, where MCS tasks can be flexibly mapped based on the moving behavior of a participant’s daily activities. A sequential behavior model is first proposed to describe the moving behavior of a participant’s daily activities. Specifically, the behavior of a daily activity is represented by the spatiotemporal trajectory distribution, and a sequential behavior graph is adopted to model the relationships among multiple activities. To extract the sequential behavior from historical trajectories, a projection-clustering (PC) algorithm and a behavior sequentializing approach are developed. Then, based on the extracted sequential behavior, a behavior-to-task mapping network is proposed to evaluate the fitness between tasks and a participant. Finally, extensive simulations are conducted to demonstrate the functionality and performance of BETA. The results show that our proposed method outperforms the state-of-the-art solutions in the mapping efficiency of tasks with different sensing scales.

Mobile crowdsensing (MCS) is a popular paradigm to collect sensed data for numerous sensing applications. With the increment of tasks and workers in MCS, it has become indispensable to design efficient task allocation schemes to achieve high performance for MCS applications. Many existing works on task allocation focus on single-task allocation, which is inefficient in many MCS scenarios where workers are able to undertake multiple tasks. On the other hand, many tasks are time-limited, while the available time of workers is also limited. Therefore, time validity is essential for both tasks and workers. To accommodate these challenges, this paper proposes a multi-task allocation problem with time constraints, which investigates the impact of time constraints to multi-task allocation and aims to maximize the utility of the MCS platform. We first prove that this problem is NP-complete. Then two evolutionary algorithms are designed to solve this problem. Finally, we conduct the experiments based on synthetic and real-world datasets under different experiment settings. The results verify that the proposed algorithms achieve more competitive and stable performance compared with baseline algorithms.

Data aggregation is a fundamental problem in mobile crowdsensing (MCS). However, the existing approaches are still unsatisfactory considering the privacy protection of sensing data and aggregation results. In addition, most existing privacy-preserving data aggregation schemes can only support a single type of aggregation, which limits their application scenarios. To address these issues, we propose a novel privacy-preserving and customization-supported data aggregation scheme that can achieve multiple types of aggregation. Specifically, we utilize additive secret sharing ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> ) to protect the privacy of both sensing data and aggregation results and then propose a simplified secure triplet generation protocol based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {ASS}$ </tex-math></inline-formula> to construct secure aggregation operations. Moreover, we design a secure comparison (SC) algorithm and a secure top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> algorithm to realize customized aggregation (i.e., statistical aggregation over top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> largest or smallest values of sensing data). The formal theoretical analysis demonstrates that the proposed scheme is effective, and the extensive experiments conducted on a real-world data set show that the proposed approach is privacy preserving and efficient.

Mobile crowd sensing (MCS) is a novel example of urban sensing, which uses smart phones, smart wearable devices, on-board sensors and other mobile sensor devices to collect data, and is widely used in intelligent transportation, medical care, environmental monitoring and other fields. A growing number of studies show that a good task allocation mechanism is an indispensable key factor in determining whether MCS applications can obtain good service quality. In this paper, we introduce the realistic background of MCS and combine some specific applications to exhaustively classify task allocation problems in MCS. Then, the typical algorithm for MCS task allocation is classified and illustrated. Finally, the current research challenges and future research directions are presented.

개인정보자기결정권은 헌법전에 명문으로 규정되지 않았음에도 불구하고 헌법해석에 의해 기본권으로 인정되는 기본권이다. 그리고 개인정보자기결정권이라는 기본권은 정보화시대에 정보주체의 사생활 보호 등 여러 보호영역과의 관련성을 갖는 중요한 기본권이다. 그런데 메르스 사태 이후 현재의 코로나19 상황에 이르기까지 감염병 예방과 전파방지의 과정 속에서 개인정보의 제한과 침해에 대한 숙고와 검토가 필요하다는 것이 자명해졌다. 왜냐하면 실제로 코로나 19에 대한 효과적 대응을 위한 정보공개 시스템 실행의 이면에는 확진환자 개인의 사생활이 여과 없이 공개되는 부작용 발생이 적지 않았기 때문이다. 이미 국가인권위원회도 확진자의 과도한 사생활 노출로 인한 인권침해가 발생하지 않도록 해야 한다고 권고한 바 있다. 이러한 상황에 대한 문제의식을 바탕으로 이 글에서는 개인의 민감정보 등 개인정보 공개와 관련한 개인정보 보호법과 감염병 예방법의 관련 규정들을 상관적으로 검토하였다. 특히 개인정보자기결정권과 관련하여 감염병 예방법상의 정보공개와 관련된 규정인 제34조의2 규정을 중심으로, 확진자의 내밀한 사생활을 보호할 수 있는 세부적이고 합리적인 공개기준의 정립 여부와 특히 위치정보 등의 공개로 인한 권리침해의 문제에 대해 살펴 보았다. 일반적인 개인정보와 구분되는 개인위치정보의 특성을 고려할 때 개인위치정보가 개인정보 보호법상의 민감정보에 해당될 수 있는가, 개인위치정보의 분류에 관하여 온라인행태정보유형으로 생존하는 개인에 관한 정보로써 개인정보 가명가공정보익명가공정보 중 어디에도 해당하지 않는 정보유형으로 새로이 “개인관련정보”를 입안하는 방안의 검토, 감염병 예방법상 개인위치정보를 비롯한 개인정보를 제공하고 공개함으로써 발생될 수 있는 권리침해에 관한 대책과 절차에 대해 검토하였다. 개인정보에 관련된 법제는 그 정보의 보호와 이용이 동시에 고려되어야 할 필요가 있다. 즉 개인정보 관련법제는 ‘개인정보의 유용성 배려’와 ‘개인의 권리이익 보호’의 균형을 맞추는 병존적 구조를 반영한 것이어야 할 것이다. 다만 양법익 간의 관계에 있어서는 ‘개인의 권리이익 보호’가 우선적으로 고려되는 것이어야만 함은 물론이다. 안전사회 추구로 인한 부작용을 간과해서는 안되는 것도, 개인정보의 유통으로 인한 비재산적 손해에 대한 배상책임을 고민해야 하는 것 도 이러한 헌법정신에 기인한다고 하겠다. 감염병 예방법의 새로운 수정이 시작되어야 한다.The right to self-determination of personal information is a basic right recognized as a basic right by constitutional interpretation, even though it was not prescribed in the Constitution. And this right is an important basic right that has relevance to various areas of protection, such as protection of the privacy of the information subject in the information age. However, from the MERS outbreak to the current COVID-19, it has become apparent that it is necessary to consider and review the restrictions and infringements of personal information in the process of preventing infectious diseases and preventing transmission. This is because, in fact, behind the implementation of the information disclosure system for effective response to COVID-19, there were not a few occurrences of side effects in which the personal life of confirmed patients was disclosed without filtering. The National Human Rights Commission of Korea has already recommended that human rights violations should not occur due to excessive exposure of the confirmed person s private life. Based on the awareness of the problem in this situation, in this article, the related regulations of the Personal Information Protection Act and the Infectious Disease Control and Prevention Act related to the disclosure of personal information, such as personal sensitive information, were reviewed correlatively. In particular, with respect to the right to self-determination of personal information, detailed and reasonable disclosure standards have been established to protect the confidential personal life of the confirmed person, with a focus on Article 34-2, which is a regulation related to the disclosure of information in the Infectious Disease Prevention Act, and, in particular, disclosure of location information, etc., I reviewed the problem of infringement of rights caused by. Considering the characteristics of personal location information that are distinguished from general personal information, can personal location information correspond to sensitive information of the Personal Information Protection Act?, review of a plan to devise a new “personal-related information” as an information type, review measures and procedures for rights infringement that may occur by providing and disclosing personal information including personal location information in the Infectious Disease Control and Prevention Act. Legal systems related to personal information need to consider the protection and use of the information at the same time. In other words, the legislation related to personal information should reflect the coexistence structure that balances “consideration of the usefulness of personal information” and “protection of individual rights and interests”. However, in the relationship between the two legal interests, ‘protection of individual rights and interests’ should be given priority. It can be said that this constitutional spirit should not overlook the side effects caused by the pursuit of a safe society and the need to consider liability for compensation for non-property damages caused by the distribution of personal information. A new modification of the Infectious Disease Control and Prevention Act must be initiated.

Mobile crowd sensing (MCS) assumes a collaborative effort from mobile smartphone users to sense and share their data needed to fulfill a given MCS objective (e.g., modeling of urban traffic or wellness index of a community). In this paper, we investigate the user's perception of anonymity in MCS and factors influencing it. We conducted a 4-week extensive smartphone user study to fulfill three main objectives. (1) Understand if users prefer to share data anonymously or not anonymously. (2) Investigate the possible factors influencing the difference between these two modalities, considering: (a) users' sharing attitude, (b) shared data kind and (c) users' intimacy when data are shared (we defined intimacy as the users' perception of their context with respect to place, number and kind of people around them). (3) Identify further users' personal factors influencing their perception of anonymity via multiple interviews along the user study. In the results, we show that data are shared significantly more when anonymously collected. We found that the shared data kind is the factor significantly contributing to this difference. Additionally, users have a common way to perceive anonymity and its effectiveness. To ensure the success of anonymization algorithms in the context of MCS systems, we highlight which issues the researchers developing these algorithms should carefully consider. Finally, we argue about new research paths to better investigate the user perception of anonymity and develop anonymous MCS systems that users are more likely to trust based on our findings.

Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint.