A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications. Environmental and user-centric sensor data of unprecedented quantity and quality can be captured and reported by a possible user base of billions of mobile phone subscribers worldwide. The strong focus on the collection of detailed sensor data may however compromise user privacy in various regards, e.g., by tracking a user's current location. In this survey, we identify the sensing modalities used in current participatory sensing applications, and assess the threats to user privacy when personal information is sensed and disclosed. We outline how privacy aspects are addressed in existing sensing applications, and determine the adequacy of the solutions under real-world conditions. Finally, we present countermeasures from related research fields, and discuss their applicability in participatory sensing scenarios. Based on our findings, we identify open issues and outline possible solutions to guarantee user privacy in participatory sensing. <s> BIB001 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction. <s> BIB002 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking. <s> BIB003 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users' mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces. <s> BIB004 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> Modern technology generates such a huge amount of public and private datasets that its security becomes an inevitable task. Initially the priority was provided for data security for the data of organization's and firm's, but nowadays it is necessary to provide security for personal data as well. So to achieve the data security, it is mandatory to preserve the privacy of personal information for that we use anonymization technique such as generalization, bucketization, multi set-based generalization, one-attribute-per-column slicing, slicing and suppression with slicing are applied to avoid retrieval of data from database. Thus, privacy preservation means to protect the data value and it is used for data mining in order to get the valid and accurate results. These are discussed and successfully analyzed with different parameters such as revealed co-relation quality (linkage property), loss of information, type of data, security (privacy preserved) and membership disclosure in this paper. The analysis shows that suppression with slicing is an innovative technique that preserves the privacy of identity of an individual in a database better than previously mentioned techniques. <s> BIB005 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> The plummeting cost of Bluetooth tags and the ubiquity of mobile devices are revolutionizing the traditional lost-and-found service. This paper presents SecureFind, a secure and privacy-preserving object-finding system via mobile crowdsourcing. In SecureFind, a unique Bluetooth tag is attached to every valuable object, and the owner of a lost object submits an object-finding request to many mobile users via the SecureFind service provider. Each mobile user involved searches his vicinity for the lost object on behalf of the object owner who can infer the location of his lost object based on the responses from mobile users. SecureFind is designed to ensure strong object security such that only the object owner can discover the location of his lost object as well as offering location privacy to mobile users involved. The high efficacy and efficiency of SecureFind are confirmed by extensive simulations. <s> BIB006 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> In this paper, we propose a trust-based vehicular platoon crowdsensing scheme, named TripSense, in VANET. The proposed TripSense scheme introduces a trust-based system to evaluate vehicles’ sensing abilities and then selects the more capable vehicles in order to improve sensing results accuracy. In addition, the sensing tasks are accomplished by platoon member vehicles and preprocessed by platoon head vehicles before the data are uploaded to server. Hence, it is less time-consuming and more efficient compared with the way where the data are submitted by individual platoon member vehicles. Hence it is more suitable in ephemeral networks like VANET. Moreover, our proposed TripSense scheme integrates unlinkable pseudo-ID techniques to achieve PM vehicle identity privacy, and employs a privacy-preserving sensing vehicle selection scheme without involving the PM vehicle’s trust score to keep its location privacy. Detailed security analysis shows that our proposed TripSense scheme not only achieves desirable privacy requirements but also resists against attacks launched by adversaries. In addition, extensive simulations are conducted to show the correctness and effectiveness of our proposed scheme. <s> BIB007 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange. <s> BIB008 </s> A Survey on Mobile Crowd-Sensing and Its Applications in the IoT Era <s> B. PRIVACY AND SECURITY <s> Ubiquity of mobile devices with rich sensory capabilities has given rise to the mobile crowd-sensing (MCS) concept, in which a central authority (the platform) and its participants (mobile users) work collaboratively to acquire sensory data over a wide geographic area. Recent research in MCS highlights the following facts: 1) a utility metric can be defined for both the platform and the users, quantifying the value received by either side; 2) incentivizing the users to participate is a non-trivial challenge; 3) correctness and truthfulness of the acquired data must be verified, because the users might provide incorrect or inaccurate data, whether due to malicious intent or malfunctioning devices; and 4) an intricate relationship exists among platform utility, user utility, user reputation, and data trustworthiness, suggesting a co-quantification of these inter-related metrics. In this paper, we study two existing approaches that quantify crowd-sensed data trustworthiness, based on statistical and vote-based user reputation scores. We introduce a new metric—collaborative reputation scores—to expand this definition. Our simulation results show that collaborative reputation scores can provide an effective alternative to the previously proposed metrics and are able to extend crowd sensing to applications that are driven by a centralized as well as decentralized control. <s> BIB009
Another research challenge in MCS is the authenticity and integrity of the data collected from different users participating in the system. Privacy and security are very important in MCS applications that collect data related to the participant. It can be categorized into two types, user security and system security. From the system point of view, authentication and integrity verification of the information provided is critical, as this information will lead to decision making, which, if wrong, could have negative impact on the whole sensing platform. From the user point of view, this information is also critical as it may affect the user's privacy. Hence, it remains secure at the back-end processor. Each one of the above challenges could be a starting point for future research. In the following we provide some examples of existing approaches addressing both categories. Due to human involvement, MCS brings some concerns about the privacy of the user using the mobile phone as well as the people surrounding the user. Thus, data privacy mechanisms are needed at the mobile devices, the network, and the remote server to protect private data such as users' location, vital signs, images . . . etc. For example, GPS sensor readings are utilized to have private information about the participant, such as daily movements as well as home and work locations. The collected GPS sensor data is shared within the community and can be used in a given city to obtain traffic congestion levels. Hence, it is very important to protect or maintain the participant privacy by not sharing his sensitive information BIB002 , BIB001 while still enabling MCS applications. To preserve privacy, anonymization is a technique to protect and secure the user's personal data, which seeks to secure the sensitive data and identity of record owners. It is the process of either removing or encrypting the information between participants and other parts to achieve the privacy and remain anonymous. Nonetheless, without the support of policies, processes, and people, the implementation of only data anonymization will be insufficient. Some companies fairly managed to implement data anonymization on a small scale using SQL scripts efficiently to encrypt data BIB008 . Some other companies have failed after obtaining the best data-masking tool BIB005 . Anonymization consists of techniques and procedures for data processing, algorithms, keys, and data life cycle. For privacy reasons, personal identifiable information (PII) needs to be anonymous for testing and analysis. There are several anonymization techniques such as 1) Generalization, which replaces the date of birth with reduced data size, such as the year of birth only, 2) Replacement such as using an alternative identification number, 3) Perturbation, introducing random changes to the data, 4) Suppression, which deletes from the released data partially or completely. However, these techniques can be applied only on at-rest or visible data i.e. logs, data exports, web pages BIB008 , BIB005 . When malicious participants contribute with inaccurate sensor data (e.g., fake GPS readings), this could affect the integrity of the data collected from the system, which is a serious problem that could lead to lack of trust in the MCS application. Some researchers have worked on developing new approaches to resolve this problem. Some of these approaches depend on co-located infrastructure, while others depend on participation from the fixed expensive infrastructure, which could be expensive and not available sometimes. Another approach is based on trusted sensor hardware (sign) on a mobile device. These approaches need to be addressed to make sure that the integrity of the sensor data generated by the participants is reliable to provide significant decisions from the aggregate sensor data BIB003 , BIB006 , BIB001 , BIB004 . Trust-based scores can be used and updated dynamically in order to quantify the trust level of each participant in MCS and thus deal with malicious participants (or simply participants sending erroneous measurements due to some sensor defect). Several reputation and trustworthiness metrics could be investigated BIB009 . Furthermore, distributed or localized trust management can be performed to reduce the load on the central server. For example, in BIB007 , crowd-sensing in a vehicular ad hoc network (VANET) is studied, and the platoon head vehicle pre-processes the measurements from platoon members based on trust scores before forwarding the trustworthy ones to the server.