In recent years, cognitive Internet of Things (CIoT) has received considerable attention because it can extract valuable information from various Internet of Things (IoT) devices. In CIoT, truth ...discovery plays an important role in identifying truthful values from large scale data to help CIoT provide deeper insights and value from collected information. However, the privacy concerns of IoT devices pose a major challenge in designing truth discovery approaches. Although existing schemes of truth discovery can be executed with strong privacy guarantees, they are not efficient or cannot be applied in real-life CIoT applications. This article proposes a novel framework for lightweight and privacy-preserving truth discovery called LPTD-I, which is implemented by incorporating fog and cloud platforms, and adopting the homomorphic Paillier encryption and one-way hash chain techniques. This scheme not only protects devices’ privacy, but also achieves high efficiency. Moreover, we introduce a fault tolerant (LPTD-II) framework which can effectively overcome malfunctioning CIoT devices. Detailed security analysis indicates the proposed schemes are secure under a comprehensively designed threat model. Experimental simulations are also carried out to demonstrate the efficiency of the proposed schemes.
•LPTD-I is proposed to defend against false data injection attacks.•An enhanced scheme, named LPTD-II, is presented to achieve fault tolerance.•Security analysis indicates the proposed schemes are secure.•Experimental simulations demonstrate the efficiency of the proposed schemes.
Truth discovery as an effective method to improve data quality in mobile crowd sensing has recently gained widespread attention. It inferred participant weight based on the sensory data submitted by ...participants, and then used the weight to aggregate sensory data and finally inferred the real information. Due to participants in mobile crowd sensing facing the problem of privacy leakage, existing work mainly focuses on sensory data privacy, with less consideration of weight privacy. Based on this, this paper proposes a lightweight privacy-preserving truth discovery in mobile crowd sensing ALPPTD. ALPPTD ran the encryption and decryption calculations of weight and truth update on the cloud server side, which greatly reduced the computation overhead of participants to motivate more users to participate. Meanwhile, two non-colluding cloud servers use homomorphic encryption to achieve aggregation of sensory data, thus iteratively computing the truth while guaranteeing the privacy of participants’ sensory data and weights. Theoretical analysis and experiment results show that ALPPTD ensures the privacy of participants’ sensory data and weight while computing the truth value with low computation overhead characteristics of participants.
Mobile crowdsensing has become a novel and promising paradigm in collecting, analyzing, and exploiting massive amounts of data. However, the issue of data quality has not been carefully addressed. ...Low quality data contributions undermine the effectiveness and prospects of crowdsensing, and thus motivate the need for approaches to guarantee the high quality of the contributed data. In this paper, we integrate quality estimation and monetary incentive, and propose a quality-based truth estimation and surplus sharing method for crowdsensing. Specifically, we design an unsupervised learning approach to quantify the users' data qualities and long-term reputations, and exploit an outlier detection technique to filter out anomalous data items. Furthermore, we model the process of surplus sharing as a co-operative game, and propose a Shapley value-based method to determine each user's payment. We have conducted a real crowdsensing experiment and a large-scale simulation to evaluate our method. The evaluation results show that our approach achieves good performance in terms of both quality estimation and surplus sharing.
Mobile crowdsensing enables convenient sensory data collection from a large number of mobile devices and has found various applications. In the real practice, however, the sensory data collected from ...various mobile devices are usually unreliable. To extract truthful information from the unreliable sensory data in mobile crowdsensing, the topic of truth discovery has received wide attention recently, which essentially operates by estimating user reliability degrees and performing reliability-aware truthful aggregation. Despite the effectiveness, applying truth discovery in mobile crowdsensing faces several privacy and security challenges. First, the sensory data and reliability degrees of users may reveal privacy-sensitive information and, thus, demand strong protection. Second, the requester that initiates a crowdsensing application usually needs to have monetary investment, so the inferred truths can be the requester's proprietary information and should be protected as well. In this paper, we propose a new system architecture enabling encrypted truth discovery in mobile crowdsensing. We focus on general and realistic mobile crowdsensing scenarios with varying levels of user participation, and our security design is built on the confidence-aware truth discovery (CATD) approach for its state-of-the-art accuracy in such scenarios. In our system architecture, users send encrypted sensory data to the cloud, where CATD is then conducted in the encrypted domain. The final encrypted inferred truths are sent to the requester for decryption. Along the whole workflow, the sensory data and reliability degrees of users, as well as the inferred truths of the requester, are kept private. Extensive experiments over real-world mobile crowdsensing dataset show that our design achieves practical performance on mobile devices.
Truth discovery is an efficient technique for tackling data conflict problems in crowd sensing for distributed data collection. As the sensory data to be collected may include sensitive information ...about users, privacy-preserving truth discovery has attracted significant attention in recent years. Most existing studies apply a centralized architecture based on a cryptographic system, which may be vulnerable to single-point attacks and also has a very high computational cost. In this paper, we propose DPriTD, a decentralized privacy-preserving framework for truth discovery in crowd sensing. The proposed approach leverages the additively homomorphic property of Shamir's Secret Sharing scheme to protect user's privacy. DPriTD provides a strict privacy guarantee for crowd sensing applications. Because each sensitive data point, considered to be a secret, is split into a batch of shares, and the secret cannot be recovered unless a sufficient number of shares are aggregated, DPriTD achieves effective truth discovery while protecting sensitive data from collusion attacks. Furthermore, DPriTD is independent of a centralized server and can perform reliably when not all participants are online in real time. It thus enhances the robustness of a crowd sensing system. Extensive experiments conducted on real-world datasets demonstrate the high performance of our method compared with existing mechanisms.
•DPriTD is a decentralized privacy-preserving framework for truth discovery in crowd sensing.•DPriTD leverages the additively homomorphic property of Shamir's Secret Sharing scheme to protect user's privacy.•DPriTD achieves effective truth discovery while protects sensitive data from collusion attacks.
To obtain reliable results from conflicting data in mobile crowdsensing, numerous truth discovery protocols have been proposed in the past decade. However, most of them do not consider the data ...privacy of entities involved (e.g., workers and servers), and several existing privacy-preserving truth discovery protocols either provide limited privacy protection or have heavy computation and communication overheads due to iterative computation and transmission over large ciphertexts. In this paper, we aim to propose privacy-preserving and lightweight truth discovery protocols to tackle the above problems. Specifically, we carefully design an anonymization protocol named AnonymTD to delink workers from their data, where workers' data are computed and transmitted without complicated encryption. To further reduce each worker's overheads in the scenarios where workers are willing to share their weights, we resort to the perturbation technology to propose a more lightweight truth discovery protocol named PerturbTD. Based on workers' perturbed data, two cloud servers in PerturbTD complete most of the workload of truth discovery together, which avoids the frequent involvement of workers. The theoretical analysis and the comparative experiments in this paper demonstrate that our two protocols can achieve our security goals with low computation and communication overheads.
Truth discovery has received considerable attention in mobile crowdsensing systems. In real practice, it is vital to resolve conflicts among a large amount of sensory data and estimate the truthful ...information. Although truth discovery has been widely explored to improve aggregation accuracy, numerous security and privacy issues still need to be addressed. Existing schemes either do not guarantee the privacy of each participating user, or fail to consider practical needs in crowdsensing systems. In this paper, we present two reliable and privacy-preserving truth discovery schemes for different scenarios. Our first design is fit for applications where users are relatively stable. By employing the homomorphic Paillier encryption, one-way hash chain, and super-increasing sequence techniques, this approach not only guarantees strong privacy, but also is highly efficient and practical. Our second design suits applications where users are frequently moving. In such an application, we explore data perturbation and homomorphic Paillier encryption to shift all user workloads to the server side, without compromising users' privacy. Through detailed security analysis, we demonstrate that both schemes are secure, practical, and privacy-preserving. Moreover, extensive experiments based on real world and simulated mobile crowdsensing systems, we demonstrate the efficiency of our proposed schemes.
With the advancement of mobile crowd sensing systems and vehicular ad hoc networks, the human-carried mobile devices (e.g., smartphones, smart navigators, and smart tablets) equipped with a variety ...of sensors (such as GPS, accelerometer, and compass) can work together to collect sensory data consequently delivered to the cloud for processing purposes, which supports a wide range of promising applications such as traffic monitoring, path planning, and real-time navigation. To ensure the authenticity and privacy of data, privacy-preserving truth discovery has attracted much attention since it can find reliable information among uneven quality of data collected from mobile users, while protecting both the confidentiality of users' raw sensory data and reliability. However, these methods always incur tremendous overhead and require all participants to keep online for interacting frequently with the cloud server. In this paper, we design an efficient and privacy-preserving truth discovery (EPTD) approach in mobile crowd sensing systems, which can tolerate users offline at any stage, while guaranteeing practical efficiency and accuracy under working process. More notably, our EPTD is the first solution to resolve the problem that users must be online all times during the truth discovery under a single cloud server setting. Moreover, we design a double-masking protocol to ensure the strong security of users' privacy even if the cloud server colludes with multiple users. Extensive experiments conducted on real-world mobile crowd sensing systems also demonstrate the high performance of our proposed scheme compared with existing models.
The continuous development of mobile sensing devices enables them to quickly perceive a large amount of data, forming a promising mobile crowd sensing (MCS) platform for large-scale data collection. ...The raw data stored in the data platform is further synthesized into a variety of Internet of Things (IoT) services to data consumers through the processing of the data platform. However, due to the wide range of data sources, sensing devices may contribute corrupted data, or maliciously spread forged data, causing the data platform to make wrong decisions and damage the quality of service. Therefore, collecting high-quality data is critical for the security of the data platform and the quality of IoT applications. In this paper, a novel Spatiotemporal Correlation Truth Discovery (SCTD) scheme is proposed, which adopts historical data as verifiable evidence to identify the truth of reported data and gain the trust of workers, consequently recruiting high-trust devices to collect data. First, Unmanned Aerial Vehicles (UAVs) are sent to collect Gold Ground Truth Data (GGTD), which is used as the benchmark to verify the data truth of the minority sensing devices. Then a trust evaluation method is proposed to calculate the trust of devices. Second, the data reported by trusted devices as Silver Ground Truth Data (SGTD) is utilized to verify the trust of most devices, so the method proposed in this paper can discover the truth of massive data. Third, to reduce the cost of truth discovery, a low-cost method of data fitting is proposed to collect massive historical data of the trusted device, thereby verifying the truth of data in the same time and space. Since historical data contributes little value to IoT services, the platform can obtain a large amount of historical data by paying low rewards to the devices. Finally, we propose to select mobile sensing devices to collect truthful data in different spaces, which can effectively cover the spatiotemporal correlation data truth discovery in time and space, thereby verifying as much data submitted to the platform as possible. Based on the trust relationships constructed in this paper, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy workers to participate in data-sensing tasks. The experimental results show that our solution can accurately identify the trust of more workers and verify the truth of data in a wider range while minimizing the cost of the data platform.
•A novel Spatiotemporal Correlation Truth Discovery (SCTD) scheme is proposed to identify the truth of data.•A trust evaluation method is proposed to calculate the trust of devices based on the truthful data of trusted devices.•A low-cost method is proposed to collect historical data as verifiable evidence.•Mobile devices are selected to extend the spatiotemporal coverage of truth discovery.•Experimental results show the proposed scheme can effectively identify the truth of the data in a wide range.
With the proliferation of social sensing, large amounts of observation are contributed by people or devices. However, these observations contain disinformation. Disinformation can propagate across ...online social networks at a relatively low cost, but result in a series of major problems in our society. In this survey, we provide a comprehensive overview of disinformation and truth discovery in social sensing under a unified perspective, including basic concepts and the taxonomy of existing methodologies. Furthermore, we summarize the mechanism of disinformation from four different perspectives (i.e., text only, text with image/multi-modal, text with propagation, and fusion models). In addition, we review existing solutions based on these requirements and compare their pros and cons and give a sort of guide to usage based on a detailed lesson learned. To facilitate future studies in this field, we summarize related publicly accessible real-world data sets and open source codes. Last but the most important, we emphasize potential future research topics and challenges in this domain through a deep analysis of most recent methods.
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