Privacy-Preserving and Customization-Supported Data Aggregation in Mobile Crowdsensing

Enabling privacy preserving outsourced data aggregation is regarded as an important issue for multi-domain wireless networks. In this paper, we present a novel hybrid cloud based privacy preserving outsourced data aggregation framework. To achieve this, we introduce a hybrid storage cloud and aggregator cloud architecture, in which both of the storage clouds and aggregator cloud are assumed to be untrusted but they cannot collude with each other. Based on this security assumption, we propose two novel protocols, including the pro-active privacy preserving data aggregation and passive privacy preserving data aggregation schemes, which are based on the idea of secret sharing. The pro-active scheme allows the user to pro-actively split their data to multiple storage clouds to avoid data leaking while the passive scheme allows the users to store their encrypted data in storage cloud and aggregator to finish the data aggregation based on the encrypted data. The detailed performance simulations are given to demonstrate the security, effectiveness and efficiency of the proposed scheme.KeywordsPrivacy AggregationCloud ComputingParticipatory Sensing

In Mobile Crowd Sensing (MCS), privacy and quality are two pivotal issues. Specifically, Bilateral Location Privacy Preservation (BLPP) and Bilateral Data Privacy Preservation (BDPP) represent the most critical dual-bilateral privacy in MCS. However, recruiting high-quality workers typically necessitates calculating the spatial proximity of each worker-task pair and the trustworthiness of the reported data, which tends to introduce significant dual-bilateral privacy risks. Additionally, due to the lack of prior knowledge about the workers' trustworthiness at the initial stage, the platform also faces the exploration-exploitation dilemma in worker recruitment. Existing work either overlooks the service quality of worker recruitment or fails to preserve the dual-bilateral privacy, making these two issues have not yet been adequately addressed. To bridge the gaps by addressing the associated challenges, this paper proposes a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>Q</u></b>uality-aware <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>D</u></b>ual-<bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>B</u></b>ilateral <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>P</u></b>rivacy <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>P</u></b>reserving (Q-DBPP) scheme, for upholding service quality under both BLPP and BDPP. Specifically, we present two perturbation-based privacy preservation stages to calculate Degree of Proximity (DoP) and Degree of Trust (DoT) while safeguarding BLPP and BDPP, respectively. Meanwhile, to address the exploration-exploitation dilemma, we employ an upper confidence bound-based high-quality worker recruitment stage, which integrates the estimated DoT and DoP into the reverse auction to comprehensively optimize service quality. To the best of our knowledge, our Q-DBPP scheme is the first to simultaneously achieve high service quality and dual-bilateral privacy in MCS. Theoretical analyses and extensive experiments validate the superior performance of our Q-DBPP scheme in terms of privacy, incentive, efficiency, and quality.

Providing efficient data aggregation while preserving data privacy is a challenging problem in wireless sensor networks research. In this paper, we present two privacy-preserving data aggregation schemes for additive aggregation functions. The first scheme -cluster-based private data aggregation (CPDA)-leverages clustering protocol and algebraic properties of polynomials. It has the advantage of incurring less communication overhead. The second scheme -Slice-Mix-AggRegaTe (SMART)-builds on slicing techniques and the associative property of addition. It has the advantage of incurring less computation overhead. The goal of our work is to bridge the gap between collaborative data collection by wireless sensor networks and data privacy. We assess the two schemes by privacy-preservation efficacy, communication overhead, and data aggregation accuracy. We present simulation results of our schemes and compare their performance to a typical data aggregation scheme -TAG, where no data privacy protection is provided. Results show the efficacy and efficiency of our schemes. To the best of our knowledge, this paper is among the first on privacy-preserving data aggregation in wireless sensor networks.

Worker selection is always one of the most fundamental problems in mobile crowdsensing (MCS), since the reliability of workers' sensing data is hugely significant to the service quality. In the worker selection process, it is inevitable for the workers to share some of their sensitive information. Consequently, numerous studies are conducted on the problem of privacy-preserving worker selection in MCS platforms. However, most of the existing methods focus on static and short-term situations. As a result, they are inapplicable to the highly dynamic environments where the MCS tasks are long term and the workers can continuously arrive at/leave the system. To solve these problems, in this article, we propose a privacy-preserving worker selection scheme based on the probabilistic skyline over sliding windows. Specifically, the proposed scheme can select reliable workers for each current sliding window in terms of working experience, expiry time, and trustability. Besides, we design an ElGamal encryption-based scheme for securely outsourcing and comparing workers' personal information without revealing their privacy. Detailed security analysis shows that the workers' sensitive information, e.g., working experience and trustability, are not revealed to any authorized parties during the process of MCS under our security model. Furthermore, extensive experiments on both real-world and simulated data sets demonstrate that our proposed scheme outperforms the baseline method in two application scenarios, i.e., 1) continuous worker arrival and 2) continuous worker departure.

Many wireless sensor network (WSN) applications require privacy of the sampled data during transmission from the source nodes to a data collecting device, say a query server. Providing an efficient data aggregation scheme with preserving data privacy is a challenging problem in WSNs. Although the secure data aggregation in WSNs has been well studied in the recent years, there exists a little work, for instance PDA (Privacy-preserving Data Aggregation), which focuses on protecting sensor data not only from adversaries but also from the participating trusted sensor nodes. However, PDA suffers from two problems. The first one is high communication cost due to unnecessary traffics in the network during data transmissions. The second one is high computation cost due to the use of expensive technique to customize sensor data. To resolve the problems, we, in this paper, propose a new private data aggregation scheme for WSNs. The proposed scheme applies the additive property of complex numbers in order to combine sensor data and preserve data privacy during transmissions to the query server. We show from our analytical performance evaluations that our scheme is more efficient than the PDA scheme in terms of both communication and computation costs.

Mobile crowd sensing (MCS) has key strategy for immediately monitoring situations in urban areas and connected vehicles, if the use of a devoted network of sensors is more or less feasible. By exploiting mobile devices and users of smartphones globally, MCS provides an infinite number of unique capabilities. When such a system is extremely beneficial, it requires precise data from sensing and the people who carry them (i.e., users) throughout task supervisory activities such as participant selection and work distribution. People may be hesitant to give data due to privacy concerns. To address and resolve privacy concerns, a data privacy preservation model and anomaly detection are proposed. The major intention of the model is to perform security‐based dynamic authentication to achieve privacy preservation in data. Initially, for security purposes, the machine learning algorithm Weighted Probabilistic Neural Network (WPNN) is used, in which the weight optimization is done by Improved Red Panda Optimization (IRPO). Subsequently, with the help of non‐attacked network, the dynamic authentication is performed using the Nash Equilibrium (game theory). In further stages, the privacy preservation of data is accomplished by Optimal Key‐based Attribute‐Based Encryption (OK‐ABE). Lastly, the effectiveness of the system is validated and measured across diverse metrics. Therefore, the outstanding results prove that it effectively detects the anomalies and preserves the data.

An efficient aggregation scheme resisting on malicious data mining attacks for smart grid

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

The rapid expansion of mobile devices with enhanced sensing and computing capabilities has driven the growth of mobile crowd sensing (MCS), enabling applications that collect large datasets from sources like smartphones and smartwatches. However, this data aggregation raises substantial security and privacy concerns, especially when MCS integrates with unmanned aerial vehicles (UAVs), where potential risks are further amplified. This study identifies and analyzes specific security and privacy threats in UAV-based MCS through the framework of the confidentiality, integrity, and availability (CIA) triad. We categorize potential vulnerabilities and propose comprehensive countermeasures targeting hardware, software, and communication models. Our findings outline strategic and actionable countermeasures to mitigate identified risks, thus ensuring data integrity and reliable functionality within MCS systems. Additionally, we present a security scenario involving mitigation suggested for data integrity and recovery. This work underscores the critical need for robust security frameworks in UAV-enhanced MCS applications, offering a holistic approach to mitigate emerging security threats.

Integrity protecting hierarchical concealed data aggregation for wireless sensor networks

Mobile crowdsensing (MCS) is becoming an extremely pervasive sensing paradigm with the popularization of intelligent devices, which needs users to release their data to the sensing platform. But to the MCS system, user's privacy-preserving demands may be time-varying in the data releasing process. In addition, protecting data privacy and ensuring data utility is becoming a contradictory and critical issue, which results in a trade-off problem that needs to be solved. In this article, we construct a differential game model to solve the trade-off problem between the data utility and privacy preserving in mobile crowdsensing system, and solve the feedback Nash equilibrium solutions based on the dynamic programming in the MCS system. Based on the feedback Nash equilibrium solutions, users and the platform can achieve maximization of privacy requirement and data utility, respectively. Ultimately, a numerical simulation has been made to show the correctness of the proposed differential game model.

The RSU can collect and disseminate information to enhance road traffic safety. Meanwhile, with the widely use of the sensors and wireless ad hoc networks, the VSN is formed. Combined the functions of the RSU and the VSN, the Road Probing scenario is proposed to enlarge the RSU’s information collection range. In this paper, we focus on the safety-related traffic information on freeway and the practicable data aggregation scheme for Road Probing. We conclude the most important data types to be gathered. After that, a data aggregation scheme using time converge globally and space converge locally will be proposed in Road Probing. Our method can greatly reduce data redundancy, and its advantage is confirmed by the simulation results. The data reception improves greatly and the transmission delay is comparatively low.

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

Privacy preservation of online user behavior data is important aspect in data mining, as of late, the study of privacy preserving data mining or networking has been studied mainly due to the extensive proliferation of confidential data on the internet. As online social networking sites attract thousands of million people to use it every day for different purpose, due to this lots of data is being generated every day on internet. Online user behavior analysis and security is becoming increasingly important, as it offers valuable information which can be useful for research purpose or analyst to develop better e-commerce strategies. However, it raises significant privacy concern of users data. Data aggregation scheme is generally used for privacy concern. In this paper, for privacy preservation of multiple online users behavior data, we have studied and analyzed multiple techniques. The main objective behind this survey paper is to percepting the existing privacy preservation and analysis technique of user behavior data and to find a better techniques and algorithms as a way to offer excessive efficiency and scalability. Subsequently, several methods for the privacy preservation and analysis have been suggested to attain the highest relevance.

A privacy-preserving data aggregation system based on blockchain in VANET