Privacy-preserving Level Research Articles

Visual privacy-preserving level evaluation for multilayer compressed sensing model using contrast and salient structural features

Recognition and classification tasks in images or videos are ubiquitous, but they can lead to privacy issues. People increasingly hope that camera systems can record and recognize important events and objects, such as real-time recording of traffic conditions and accident scenes, elderly fall detection, and in-home monitoring. However, people also want to ensure these activities do not violate the privacy of users or others. The sparse representation classification and recognition algorithms based on compressed sensing (CS) are robust at recognizing human faces from frontal views with varying expressions and illuminations, as well as occlusions and disguises. This is a potential way to perform recognition tasks while preserving visual privacy. In this paper, an improved Gaussian random measurement matrix is adopted in the proposed multilayer CS (MCS) model to realize multiple image CS and achieve a balance between visual privacy-preserving and recognition tasks. The visual privacy-preserving level evaluation for MCS images has important guiding significance for image processing and recognition. Therefore, we propose an image visual privacy-preserving level evaluation method for the MCS model (MCS-VPLE) based on contrast and salient structural features. The basic concept is to use the contrast measurement model based on the statistical mean of the asymmetric alpha-trimmed filter and the salient generalized center-symmetric local binary pattern operator to extract contrast and salient structural features, respectively. The features are fed into a support vector regression to obtain the image quality score, and the fuzzy c-means algorithm is used for clustering to obtain the final evaluated image visual privacy-preserving score. Experiments on three constructed databases show that the proposed method has better prediction effectiveness and performance than conventional methods.

A group authentication and privacy-preserving level for vehicular networks based on fuzzy system

Vehicular ad hoc networks play an important role in current intelligent transportation networks, which have attracted much attention from academia and industry. Vehicular networks can be implemented by Long-Term Evolution Advanced (LTE-A) networks, which have been formally defined in a series of standards by third-generation partnership projects (3GPP). Abundant challenges exist in the authentication processes in LTE-A-based vehicular networks. This paper aimed to improve the security functionality of these vehicular networks by proposing a secure and efficient group authentication and privacy-preserving scheme for vehicular networks based on fuzzy system: the group authentication and privacy-preserving level (GAPL). Compared with existing schemes, the proposed scheme can greatly reduce the number of control message transmissions from mass vehicular equipment (VEs) to the network and substantially avoid overhead in LTE-A-based vehicular networks. Privacy-preserving levels are established to protect VE privacy in authentication. Furthermore, the scheme contains security functions, including privacy preservation, non-frameability and non-repudiation verification.

A Decentralized Location Privacy-Preserving Spatial Crowdsourcing for Internet of Vehicles

With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task’s location policy privacy and providing multi-level privacy preservation for workers’ locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task’s location policy. To achieve multi-level privacy preservation for workers’ driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice.

An efficient and privacy-preserving truth discovery scheme in crowdsensing applications

Truth discovery is a reliable and effective technique to resolve conflicts of heterogeneous data and estimate user reliability in mobile crowdsensing systems. Despite its effectiveness, the widespread adoption of truth discovery requires solid privacy preservation against users’ sensory data and reliability information. Existing works of private truth discovery are primarily based on conventional cryptographic primitives, which introduce tremendous workloads on the system. In this work, we first propose an efficient and privacy-preserving truth discovery framework (EPTD-I) by adopting a novel data perturbation mechanism. EPTD-I not only protects users’ privacy but also introduces little overhead on the user side. Moreover, for high mobility environments, we improve the design with a user non-interactive scheme named EPTD-II to shift all encrypted truth discovery operations to cloud platforms. In EPTD-II, each user’s sensitive information is also kept private during the complete truth discovery procedure. Thorough security analysis demonstrates that our proposed schemes are secure and offer a high level of privacy preservation. Extensive experiments conducted on practical and simulated crowdsensing applications demonstrate the effectiveness and efficiency of the proposed schemes.

Location Privacy-Preserving Method Based on Historical Proximity Location

With the rapid development of Internet services, mobile communications, and IoT applications, Location-Based Service (LBS) has become an indispensable part in our daily life in recent years. However, when users benefit from LBSs, the collection and analysis of users’ location data and trajectory information may jeopardize their privacy. To address this problem, a new privacy-preserving method based on historical proximity locations is proposed. The main idea of this approach is to substitute one existing historical adjacent location around the user for his/her current location and then submit the selected location to the LBS server. This method ensures that the user can obtain location-based services without submitting the real location information to the untrusted LBS server, which can improve the privacy-preserving level while reducing the calculation and communication overhead on the server side. Furthermore, our scheme can not only provide privacy preservation in snapshot queries but also protect trajectory privacy in continuous LBSs. Compared with other location privacy-preserving methods such as k-anonymity and dummy location, our scheme improves the quality of LBS and query efficiency while keeping a satisfactory privacy level.

Bilateral Privacy-Preserving Utility Maximization Protocol in Database-Driven Cognitive Radio Networks

Database-driven cognitive radio has been well recognized as an efficient way to reduce interference between Primary Users (PUs) and Secondary Users (SUs). In database-driven cognitive radio, PUs and SUs must provide their locations to enable dynamic channel allocation, which raises location privacy breach concern. Previous studies only focus on unilateral privacy preservation, i.e., only PUs’ or SUs’ privacy is preserved. In this paper, we propose to protect bilateral location privacy of PUs and SUs. The main challenge lies in how to coordinate PUs and SUs to maximize their utilities provided that their location privacy is protected. We first introduce a quantitative method to calculate both PUs’ and SUs’ location privacy, and then design a novel privacy preserving Utility Maximization protocol (UMax). UMax allows for both PUs and SUs to adjust their privacy preserving levels and optimize transmit power iteratively to achieve the maximum utilities. Through extensive evaluations, we demonstrate that our proposed protocol can efficiently increase the utilities of both PUs and SUs while preserving their location privacy.

Privacy-aware cloud service composition based on QoS optimization in Internet of Things

With the advent of Internet of Things (IoT), cloud service composition problem has gotten more complex along with the composition of large number of cloud services and numerous complex requirements from the users as an NP-Hard problem. In order to compose various cloud services in the IoT context, a conceptual model is required besides an effective selection and composition model. Moreover, as an important aspect of today’s human life necessity, privacy preserving during sharing the data is a crucial concern. Therefore, privacy-aware cloud service composition along with improving the other quality of service (QoS) factors is an essential research motivation especially in the cloud based-IoT environments. In this paper, a privacy-aware cloud service composition approach with respect to QoS optimization in the IoT environment is proposed through presenting an IoT-based cloud service composition conceptual model regarding the privacy level computing model and a novel hybrid evolutionary algorithm using shuffled frog leaping algorithm (SFLA) and genetic algorithm (GA) which is called SFLA-GA. The proposed algorithm is applied to optimize the suggested service composition in terms of aggregation of different QoS factors as fitness value. Also, in order to help the users to select the proper composite service, they are categorized regarding their privacy preserving level which is obtained through a computational model. Simulation results revealed that the proposed approach improves the fitness in comparison to other contemporary algorithms.

Dynamic Privacy-preserving Collaborative Schemes for Average Computation

In this paper, we consider the privacy-preserving problem in collaborative computing. Based on a two-step average computation framework, we propose three privacy-aware schemes, all of which achieve different levels of privacy protections depending on data servers’ trust degrees. Further, by carefully designing noises injected to the distributed computing process, we obtain dynamic privacy-preserving schemes, whose privacy preserving levels are measured by Kullback-Leibler differential privacy. In addition, we prove that the proposed schemes achieve convergence in different senses. Numerical experiments are finally conducted to verify the obtained privacy properties and convergence guarantees.

CFM: collusion-free model of privacy preserving frequent itemset mining

Although many privacy preserving frequent itemset mining protocols have been proposed to preserve the privacy of participants, most of them are vulnerable against collusion. Usually, these protocols are designed for semi-honest model; where in this model, it is assumed that the participants do not deviate from the protocol. However, in real world, participants may collude with each other in order to falsify the protocol or to obtain the secret values of other parties. In this paper, we analyse the vulnerability of previous privacy preserving frequent itemset mining protocols from privacy point of view, and then, we proposes a new protocol (CFM), which preserves the privacy of participants, even in collusion state. CFM is designed for mining frequent itemsets from homogenous (horizontally partitioned) data, which not only preserves the privacy of participants in collusion states, but also shows better performance in comparison with previous works. In order to achieve this goal, CFM employs a new secret sharing and secret summation scheme, which distributes secret values among participants. Privacy preserving level of CFM is evaluated based on the disclosure of sensitive information.

CFM: collusion-free model of privacy preserving frequent itemset mining

Although many privacy preserving frequent itemset mining protocols have been proposed to preserve the privacy of participants, most of them are vulnerable against collusion. Usually, these protocols are designed for semi-honest model; where in this model, it is assumed that the participants do not deviate from the protocol. However, in real world, participants may collude with each other in order to falsify the protocol or to obtain the secret values of other parties. In this paper, we analyse the vulnerability of previous privacy preserving frequent itemset mining protocols from privacy point of view, and then, we proposes a new protocol (CFM), which preserves the privacy of participants, even in collusion state. CFM is designed for mining frequent itemsets from homogenous (horizontally partitioned) data, which not only preserves the privacy of participants in collusion states, but also shows better performance in comparison with previous works. In order to achieve this goal, CFM employs a new secret sharing and secret summation scheme, which distributes secret values among participants. Privacy preserving level of CFM is evaluated based on the disclosure of sensitive information.

Secure hash algorithm-based multiple tenant user security over green cloud environment

This paper proposes a green cloud multi-tenant trust authentication with secure hash algorithm-3 (GreenCloud-MTASHA3) scheme to eliminate the unauthorised tenant access. GreenCloud-MTASHA3 scheme provide security over the multiple tenant requests by referring the confidentiality, integrity and availability rate. Confidentiality refers to limiting the unauthorised tenant's green cloud data access using the additive homomorphic privacy property in proposed scheme. Additive homomorphic privacy property-based encryption function is developed to improve the privacy preserving level. To attain the integrity level between the tenant requests and green cloud server machine in GreenCloud-MTASHA3 scheme an encrypted trust data management process is carried out. Trustworthiness of tenant request is measured to maintain the consistency level on security with minimal computational time. The proposed scheme attains the confidentiality, integrity and availability rate on communicating task. Experiment is conducted on factors such as secure computation confidence, authorised tenant computational time and space taken on storing encrypted data.

A Privacy-Preserving Online Learning Approach for Incentive-Based Demand Response in Smart Grid

Incentive-based demand response (IDR) programs enable smart grid customers to participate in the demand reduction, triggered by system contingencies or peak load, to improve reliability, sustainability, security, and efficiency of the power grid. However, the deployment of smart meters and the increasing number of customers in IDR programs make the data generated in the smart grid at a large scale. Meanwhile, fine-grained data from smart meters can deluge customers’ lifestyle and usage pattern, posing threat to customer privacy. Therefore, privacy-preserving demand source management techniques that support increasingly large-scale datasets are in urgent need. In this paper, we propose an online privacy-preserving IDR management system, in which social welfare is maximized through recommending the optimal consumer to the utility company. Since the contexts of electricity curtailment offers from the utility company are different, an adaptive context partition method is proposed to enable the system context awareness. In addition, we cluster the customers in a tree structure to make the analyses of the customers in the cluster level and thus enable the algorithm to support the large-scale system. Furthermore, a tree-based noise aggregation method is applied to guarantee both the differential privacy of customers’ sensitive information and the utility of the data. Theoretical analysis shows that our proposal guarantees differential privacy of customers, while converging to the optimal policy in a long run. Numerical results validate that our proposed algorithm supports the large-scale dataset while striking a balance between the privacy-preserving level and social welfare.

Privacy-Preserving Online Task Allocation in Edge-Computing-Enabled Massive Crowdsensing

We propose a novel context-aware task allocation framework for mobile crowdsensing in the scenario of edge computing to enable the crowdsensing platform effectively and real-timely handle large-scale crowdsensing tasks in smart city. The task allocation performs in both cloud computing layer and edge computing layer. It aims to combine the merits of cloud and edge computing, i.e., diminishing communication latency while guaranteeing overall scheduling. The cloud layer evaluates the participants’ task-oriented reputation based on the participants’ background information, task context, and historical feedbacks (i.e., rewards) and sends the edge layer the most promising subset of participants. Then the edge layer communicates with the participants for the real-time information and makes optimization based on the task requirement (e.g., maximizing the sensing coverage under the constraint of the task budget). In the cloud layer, we propose a privacy-preserving and contextual online learning algorithm to manage the participants’ reputation. The algorithm can adapt the decision-making strategy based on previous performances of participants. In the edge layer, plenty of existing centralized task allocation strategies can be directly applied to optimize based on the participants’ real-time information. Theoretical analysis shows that our proposal achieves sublinear regret and differential privacy for both requesters and participants. Experiments results validate that our proposed algorithm supports increasing big dataset while striking a balance between the privacy-preserving level and the prediction accuracy.

Improved Kalman filter based differentially private streaming data release in cognitive computing

Abstract Cognitive computing works well based on volumes of data, which offers the guarantee of unlocking novel insights and data-driven decisions. Steaming data is a major component of aggregated data, and sharing these real-time aggregated statistics has gained a lot of benefits in decision analysis, such as traffic heat map and disease outbreaks. However, original streaming data sharing will bring users the risk of privacy disclosure. In this paper, differential privacy technology is introduced into cognitive system, and an improved Kalman filter based differentially private streaming data release scheme is proposed for privacy requirement of cognitive computing system. The feasibility of the proposed scheme has been demonstrated through analysis of the utility of sanitized data from four real datasets, and the experimental results show that the proposed scheme outperforms the Kalman filter-based method at the same level of privacy preserving.

Differentially-Private and Trustworthy Online Social Multimedia Big Data Retrieval in Edge Computing

The explosive growth of multimedia contents (MCs) in today's mobile social networks has pushed edge computing to face severe security and online big data-processing problems. On the one hand, the edge nodes (ENs) should help mobile users find, cache, and share MCs in the presence of an ever-increasing scale of multimedia big data. On the other hand, how to provide secure MC retrieval schemes to exclude dishonest-and-malicious untrusted ENs and to prevent privacy breaches from honest-but-curious ENs and users is a challenging issue. To tackle these problems, we study the privacy-preserving and trustworthy MCs retrieval system to make personalized MC recommendations from ENs to users with big data support. In our framework, each EN is modeled as a distributed context-aware online learner. ENs collaborate to learn users’ preferences based on their contexts and previous behaviors and social intimacy. To support big data analytics, we establish an MC-cluster tree from top to the bottom to handle the dynamically varying cached MC datasets. A differentially private algorithm is proposed to preserve the data privacy among honest-but-curious ENs and users. To guarantee trustworthy edge computing, a trust evaluation mechanism is designed to evaluate the reliability of ENs. We further consider the structure of edge networks to improve the performance of our algorithm. Experimental results validate that our new framework can support increasing multimedia big datasets while striking a balance among privacy-preserving level, Trustworthy level, and caching MC prediction accuracy.

Dynamic Pricing for Privacy-Preserving Mobile Crowdsensing: A Reinforcement Learning Approach

MCS is an emerging technology that exploits the enormous sensing power of widely used mobile devices to complete sensing tasks in a cost-efficient manner. Among all outstanding issues of current MCS systems, the concern about a lack of privacy protection for the sensing data of participants has drawn increasing attention recently. Various privacy-preserving MCS mechanisms have been proposed for the static scenario where users' privacy protection requirements remain unchanged. In practice, however, users' requirements for privacy protection can be time-varying, which further complicates the design of privacy-preserving MCS. In this article, we first give an overview of multiple promising approaches for privacy-preserving MCS, based on which we make a first attempt to explore privacy-preserving MCS in a dynamic scenario, which is cast as a Markov Decision Process. Specifically, we develop a reinforcement learning based approach, by which the platform can dynamically adapt its pricing policy catering to the varying privacy-preserving levels of participating users. We further use a case study to evaluate the performance of our proposed approach.

A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender Systems

Nowadays, the booming demand of big data analytics and the constraints of computational ability and network bandwidth have made it difficult for a stand-alone agent/service provider to provide suitable information for every user from the large volume online data within the limited time. To handle this challenge, a recommender system (RS) can call in a group of agents to collaborate to learn users’ preference and taste, which is known as a distributed recommender system (DRS). DRSs can improve the accuracy of a traditional RS by requesting agents to share information with each other. However, it is challenging for DRSs to make personalized recommendations for each user due to the large amount of candidates. In addition, information sharing among agents raises a privacy concern. Thus, we propose a privacy-preserving DRS in this paper, and then model each service provider as a distributed online learner with context-awareness. Service providers collaborate to make personalized recommendations by learning users’ preferences according to the user context and users’ history behaviors. We adopt the federated learning framework to help train a high quality privacy- preserving centralized model over a large number of distributed agents which is probably unreliable with relatively slow network connections. To handle big data scenario, we build an item-cluster tree to deal with online and increasing datasets from top to the bottom . We further consider the structure of social network and present an efficient algorithm to avoid more performance loss adaptively. Theoretical proofs show that our proposed algorithm can achieve sublinear regret and differential privacy protection simultaneously for service providers and users. Numerical results confirm that our novel framework can handle increasing big datasets and strike a trade-off between privacy-preserving level and the prediction accuracy.

REAP: An Efficient Incentive Mechanism for Reconciling Aggregation Accuracy and Individual Privacy in Crowdsensing

Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of incentive mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient incentive mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.

Efficient Privacy-Preserving Scheme for Real-Time Location Data in Vehicular Ad-Hoc Network

The development of vehicular ad-hoc network (VANET) allows vehicle users to enjoy location-based services through the network at any time and place. However, it also raises location privacy issues. Privacy-preservation of large amounts of real-time location data is a critical issue. Existing research work, such as the mix-zone scheme, has protected users’ location privacy and driving routes to a certain extent. However, existing schemes have some deficiencies in security and efficiency. This paper presents a novel location privacy protection scheme. Vehicles can generate virtual locations dynamically based on the situation of surrounding vehicles to provide misleading information regarding the driving route and thus achieve location privacy. We use entropy of anonymity set and tracking success ratio as metrics to measure the level of privacy-preserving. Simulation experiments have been carried out to compare with other existing schemes. The results show that entropy of anonymity set in our scheme is 100% larger than the compared scheme and the tracking success ratio has decreased from 10% to 1%. So, the results illustrate that the proposed scheme offers better security and location-based services.

Improving k-anonymity based privacy preservation for collaborative filtering

Collaborative Filtering (CF) is applied in recommender systems to predict users’ preference through filtering the information or patterns. Privacy Preserving Collaborative Filtering (PPCF) aims to achieve privacy protection in the recommendation process, which has an increasing significance in recommender systems and thus attracted much interests in recent years. Existing PPCF methods are mainly based on cryptography, obfuscation, perturbation and differential privacy. They have high computational cost, low data quality and difficulties in calibrating the magnitude of noise. This paper proposes a (p, l, α)-diversity method that improves the existing k-anonymity method in PPCF, where p is attacker’s prior knowledge about users’ ratings and (l, α) is the diversity among users in each group to improve the level of privacy preserving. To achieve (l, α)-diversity, users in each equivalence class shall come from at least l (l < k) clusters in α clustering results. Therefore, we firstly apply Latent Factor Model (LFM) to reduce matrix sparsity. Then we propose an improved Maximum Distance to Average Vector (MDAV) microaggregation algorithm based on importance partitioning to increase the homogeneity among the records in each group which can retain better data quality in (p, l, α)-diversity model. Finally, we apply t-closeness in PPCF. Theoretical analysis and experimental results demonstrate our approach assures a higher level of privacy preserving and less information loss than existing methods.