Privacy Protection Mechanisms Research Articles

Privacy Protection of Task in Crowdsourcing: Policy-Hiding and Attribute Updating Attribute-Based Access Control Based on Blockchain

Crowdsourcing is a new way to solve complex problems by using human intelligence. However, the tasks and user information privacy concerned in crowdsourcing have not been adequately addressed. It is necessary to design a privacy protection mechanism for tasks that need to be restricted to specific user groups. Ciphertext-policy attribute-based encryption (CP-ABE) is an efficient and feasible cryptographic tool, particularly for crowdsourcing systems. The encryptor can choose the access policy independently, which limits the scope of decryption users. At present, most CP-ABE schemes adopt a centralized management platform, which poses problems such as high trust-building costs, DDoS attacks, and single point of failure. In this paper, we propose a new access control scheme based on CP-ABE and blockchain, which has the properties of policy hiding and attribute updating. To protect the privacy of worker’s attributes, we adopt a test algorithm based on a fully homomorphic cryptosystem to confidentially judge whether the worker’s attribute lists match the hidden attributes policy in ciphertext or not before the decryption. Experiment results and comprehensive comparisons show that our mechanism is more flexible, private, and scalable than existing schemes.

Blockchain-Based Audio Watermarking Technique for Multimedia Copyright Protection in Distribution Networks

Copyright protection in multimedia protection distribution is a challenging problem. To protect multimedia data, many watermarking methods have been proposed in the literature. However, most of them cannot be used effectively in a multimedia distribution network (MDN) as they are not designed to support multi-layer watermark embedding. Multi-layer watermarking mechanisms were developed to protect multimedia data across different layers in an MDN. However, in those mechanisms, we need to trust the entities in the MDN, such as regional and country distributors. To overcome this potential drawback, in this article, we propose a novel privacy protection mechanism for MDNs by combining the advantages of both blockchain and watermarking technologies. A specifically designed watermarking algorithm is used to link the copyright information with the audio file, while a novel blockchain-based smart contract mechanism is developed to enforce the proper functioning of each entity in the distribution network. Moreover, the new audio mechanism is computationally efficient. Although audio signals are used to show the effectiveness of the proposed mechanism, the proposed approach can easily be extended to other multimedia objects, such as an image. The validity of the proposed mechanism is demonstrated by our simulation results. The proposed mechanism can benefit multimedia production companies and other entities in the MDN.

Trajectory Privacy Protection Mechanism based on Salp-Like Swarm Algorithm

Location-based services have been widely used in daily life, providing diversified services for users. However, users may face the risk of trajectory privacy disclosure while enjoying the convenience of location-based services. Most of the existing trajectory protection schemes cannot match the road network and are vulnerable to attacks based on background information. In this paper, the concept of salp swarm algorithm is introduced to construct salp-like swarm algorithm, which can generate K−1 false trajectories that are highly similar to real trajectories. It is difficult for attackers to distinguish them. Besides, a road network matching model is designed in order to match the proposed trajectory privacy protection algorithm with the real road network environment, so that the effect of trajectory privacy protection is improved. Morever, a false location selection mechanism is proposed to find false location points, which not only considers the location and speed of users, but also ensures that the selection of false location points is more in line with the road network environment. The experimental results show that, under the condition of satisfying the same service quality, the trajectory privacy leakage probability of this scheme is reduced by 33% compared with the existing schemes, and it has better privacy protection effect.

Intermittent Private Information Retrieval With Application to Location Privacy

We study the problem of intermittent private information retrieval with multiple servers, in which a user consecutively requests one of <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> messages from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> replicated databases such that part of requests need to be protected while others do not need privacy. Motivated by the location privacy application, the correlation between requests is modeled by a Markov chain. We propose an intermittent private information retrieval scheme that concatenates an obfuscation scheme and a private information retrieval scheme for the time period when privacy is not needed, to prevent leakage incurred by the correlation over time. In the end, we illustrate how the proposed scheme for the problem of intermittent private information retrieval with Markov structure correlation can be applied to design a location privacy protection mechanism in the location privacy problem.

LPPS-AGC: Location Privacy Protection Strategy Based on Alt-Geohash Coding in Location-Based Services

The Internet of Things (IoT) connects billions of physical devices around the world to the Internet to collect and share massive data. Location privacy leakage has received considerable attention in the field of security. In order to implement the k -anonymity location privacy protection mechanism, a previous work constructed an anonymous location set by retrieving historical request record database from the trusted third anonymous server, which advantageously protects user’s location privacy. However, the performance of location-based services (LBSs) is weakened by the time overhead of continual retrieving database. Moreover, with the increasingly flourishing of the positioning technique, user location can be accurate to the user’s altitude beyond the two-dimensional latitude and longitude coordinates. In this paper, we present a location privacy protection strategy in LBSs based on Alt-Geohash coding (LPP-AGC) method synthetically considering the altitude of user location and time overhead. Specifically, we give the Alt-Geohash coding algorithm (AGCA) to retrieve the historical request record database, which greatly reduces the time overhead and ensures the immediacy of LBSs. In addition, we propose the dummy location generating algorithm (DLGA) and location filtering algorithm (LFA) to provide users with autonomous k -anonymity location privacy protection. Extensive simulations are performed to verify the performance and security of the proposed strategy.

Introducing the Architecture of FASTER: A Digital Ecosystem for First Responder Teams

Emergency first responders play an important role during search and rescue missions, by helping people and saving lives. Thus, it is important to provide them with technology that will maximize their performance and their safety on the field of action. IFAFRI, the “International Forum to Advanced First Responder Innovation” has pointed out several capability gaps that are found in the existing solutions. Based on them, there is a need for the development of novel, modern digital solutions that will better assist responders by helping them on the field and, at the same time, better protect them. The work presented here introduces the logical architecture implemented in the Horizon 2020 project called FASTER (First responders Advanced technologies for Safe and efficienT Emergency Response), which is an innovating digital ecosystem for emergency first response teams. It is a system that meets the requirements of the consortium members but also fills all the gaps that IFARFI has pointed out and consists of mechanisms and tools for data communication, data analysis, monitoring, privacy protection and smart detection mechanisms.

Evolutionary tree-based quasi identifier and federated gradient privacy preservations over big healthcare data

&lt;span&gt;Big data has remodeled the way organizations supervise, examine and leverage data in any industry. To safeguard sensitive data from public contraventions, several countries investigated this issue and carried out privacy protection mechanism. With the aid of quasi-identifiers privacy is not said to be preserved to a greater extent. This paper proposes a method called evolutionary tree-based quasi-identifier and federated gradient (ETQI-FD) for privacy preservations over big healthcare data. The first step involved in the ETQI-FD is learning quasi-identifiers. Learning quasi-identifiers by employing information loss function separately for categorical and numerical attributes accomplishes both the largest dissimilarities and partition without a comprehensive exploration between tuples of features or attributes. Next with the learnt quasi-identifiers, privacy preservation of data item is made by applying federated gradient arbitrary privacy preservation learning model. This model attains optimal balance between privacy and accuracy. In the federated gradient privacy preservation learning model, we evaluate the determinant of each attribute to the outputs. Then injecting Adaptive Lorentz noise to data attributes our ETQI-FD significantly minimizes the influence of noise on the final results and therefore contributing to privacy and accuracy. An experimental evaluation of ETQI-FD method achieves better accuracy and privacy than the existing methods.&lt;/span&gt;

Computing resource allocation strategy considering privacy protection mechanism in edge computing environment

Aiming at the problem that it is difficult to achieve high privacy security and low time consumption in the process of user terminals resource allocation, this paper proposes a computing resource allocation strategy considering privacy protection mechanism in edge computing environment. Firstly, the problem is modelled as partially observable Markov decision process, and a reinforcement learning algorithm is proposed to minimize the failure rate of tasks. Secondly, the privacy entropy is used to protect the privacy information data, and computing tasks are divided into several different types of data to measure the uncertainty of computing tasks. Finally, the time consumption and the data privacy security degree of user terminals are quantified by using time computing model and privacy entropy value, and a multi-objective optimization problem model is established. Experiments show that the system energy consumption of the proposed method in the worst case is also less than 20 J, which is better than the two comparison methods. This method not only reduces the system energy consumption, but also enhances the security of data distribution and transmission.

Privacy-Preserving Aggregate Mobility Data Release: An Information-Theoretic Deep Reinforcement Learning Approach

It is crucial to protect users’ location traces against inference attacks on aggregate mobility data collected from multiple users in various real-world applications. Most of the existing works on aggregate mobility data are focusing on inference attacks rather than designing privacy-preserving release mechanisms, and a few differential private release mechanisms suffer from poor utility-privacy tradeoffs. In this paper, we propose optimal centralized privacy-preserving aggregate mobility data release mechanisms (PAMDRMs) that minimize the leakage from an information-theoretic perspective by releasing perturbed versions of the raw aggregate location. Specifically, we use mutual information to measure user-level and aggregate-level privacy leakage separately, and formulate leakage minimization problems under utility constraints. As directly solving the optimization problems incur exponential complexity w.r.t. users’ trace length, we transform them into belief state Markov Decision Processes (MDPs), with a focus on the MDP formulation for the user-level privacy problem. We build reinforcement learning (RL) models and leverage the efficient Asynchronous Advantage Actor-Critic RL algorithm to derive the solutions to the MDPs as our optimal PAMDRMs. We compare them with two state-of-the-art privacy protection mechanisms PDPR (context-aware local design) and DMLM (context-free centralized design) in terms of mutual information leakage and adversary’s attack success (evaluated by her expected estimation error and Jensen-Shannon Divergence-based error). Extensive experimental results on both synthetic and real-world datasets demonstrate that the user-level PAMDRM performs the best on both measures thanks to its context-aware property and centralized design. Even though the aggregate-level PAMDRM achieves better privacy-utility tradeoff than the other two, it does not always perform better than them on adversarial success, highlighting the necessity of considering privacy measures from different perspectives to avoid overestimating the level of privacy offered to users. Lastly, we discuss an alternative, fully data-driven approach to derive the optimal PAMDRM by leveraging adversarial training on limited data samples.

A Targeted Privacy-Preserving Data Publishing Method Based on Bayesian Network

Privacy-preserving data publishing (PPDP) is an essential prerequisite for data-driven AI technologies, (such as data mining, machine learning, deep learning, etc.) to extract knowledge from data safely and legally. It has, as it should be, been studied and explored as a hot topic in the last decade. However, existing privacy protection mechanisms cannot take into account the following three aspects: preventing background attack, maximizing data availability, and resisting sensitive information mining. In this work, we propose a novel privacy-preserving data publishing framework, which protects privacy by releasing simulated data instead of real data. It is explored for generating data similar to the distribution of the real data by using Bayesian network. It consists of two ingredients. First, we transform the problem of data publication into the generation process of a Bayesian network, and correspondingly, the problem of privacy leakage is transformed into one kind of Bayesian inference attack. Second, we propose a re-anonymity framework, named (d, L)-injection, which flexibly resolves the impact of increased privacy protection strength on data availability. In addition, we transplant three classical privacy-preserving strategies to the generated Bayesian network, and demonstrates the effectiveness of the method through three public data sets from multiple application domains.

PrivItem2Vec: A privacy-preserving algorithm for top-N recommendation

To significantly protect the user’s privacy and prevent the user’s preference disclosure from leading to malicious entrapment, we present a combination of the recommendation algorithm and the privacy protection mechanism. In this article, we present a privacy recommendation algorithm, PrivItem2Vec, and the concept of the recommended-internet of things, which is a privacy recommendation algorithm, consisting of user’s information, devices, and items. Recommended-internet of things uses bidirectional long short-term memory, based on item2vec, which improves algorithm time series and the recommended accuracy. In addition, we reconstructed the data set in conjunction with the Paillier algorithm. The data on the server are encrypted and embedded, which reduces the readability of the data and ensures the data’s security to a certain extent. Experiments show that our algorithm is superior to other works in terms of recommended accuracy and efficiency.

A randomized block policy gradient algorithm with differential privacy in Content Centric Networks

Policy gradient methods are effective means to solve the problems of mobile multimedia data transmission in Content Centric Networks. Current policy gradient algorithms impose high computational cost in processing high-dimensional data. Meanwhile, the issue of privacy disclosure has not been taken into account. However, privacy protection is important in data training. Therefore, we propose a randomized block policy gradient algorithm with differential privacy. In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. To solve the privacy protection problem, we add a differential privacy protection mechanism to the algorithm, and we prove that it preserves the [Formula: see text]-privacy level. We conduct extensive simulations in four environments, which are CartPole, Walker, HalfCheetah, and Hopper. Compared with the methods such as important-sampling momentum-based policy gradient, Hessian-Aided momentum-based policy gradient, REINFORCE, the experimental results of our algorithm show a faster convergence rate than others in the same environment.

Lightweight frame scrambling mechanisms for end‐to‐end privacy in edge smart surveillance

As smart surveillance has become popular in today's smart cities, millions of closed circuit television cameras are ubiquitously deployed that collect huge amount of visual information. All these raw visual data are often transported over a public network to distant video analytic centres. This increases the risk of interception and the spill of individuals' information into the wider cyberspace that risks privacy breaches. The edge computing paradigm allows the enforcement of privacy protection mechanisms at the point where the video frames are created. Nonetheless, existing cryptographic schemes are computationally unaffordable at the resource-constrained network edge. Based on chaotic methods, three lightweight end-to-end privacy-protection mechanisms are proposed: (1) a novel lightweight Sine-cosine Chaotic Map, which is a robust and efficient solution for enciphering frames at edge cameras; (2) Dynamic Chaotic Image Enciphering scheme that can run in real time at the edge; (3) a lightweight Regions of Interest Masking scheme that ensures the privacy of sensitive attributes like face on video frames. Design rationales are discussed and extensive experimental analyses substantiate the feasibility and security of the proposed schemes.

Beyond the privacy paradox: The moderating effect of online privacy concerns on online service use behavior

In the digital information era, dealing with privacy issues is problematic in related research since online activities have become an inevitable trend. Following the privacy paradox, which occurs when online services are increasingly accepted or used despite raising the level of privacy concerns of individuals, there is no need to alleviate individual privacy concerns regarding online services. Accordingly, this study aims to empirically analyze the effect of online privacy concerns, when interacting with individual innovativeness, on individual online service use behavior. For the empirical analysis, a Heckman two-step analysis is performed using South Korean data from the 2019 Korea Media Panel Survey conducted by the Korea Information Society Development Institute. The results provide evidence in contradiction of the privacy paradox. Specifically, the main findings of this study are as follows. First, use of online services and privacy concerns are not a contradictory phenomenon both in principle and behavior but can act as a negative influence or constraint. Second, individuals with high levels of innovativeness actively use online services owing to differences in their acceptance and use of innovation. Third, as online activities become more common, privacy concerns are likely to affect the level of online service use by interacting with other personality traits. As a result, privacy concerns are more likely to act as an influencing variable that moderates the degree or intensity of an individual's use of an online service rather than an independent variable for the use of an online service. The impact of privacy concerns of individuals on the use of online services identified in this study suggests there is a need for an adequate governing mechanism for privacy protection to realize service provision through e-government.

A Data Aggregation Privacy Protection Algorithm Based on Fat Tree in Wireless Sensor Networks

Wireless sensor network is the momentous part of the Internet of Things. Data aggregation technology is the most practical method to reduce the amount of communication among nodes. Adding a privacy protection mechanism to data aggregation is one of the important means for privacy protection and security in wireless sensor networks. Aiming at certain performance defects of the existing SMART (Slice-Mix-AggRegaTe) privacy protection algorithms, a fat tree-based data aggregation privacy protection algorithm is proposed in this paper, which is referred to as the FTSMART (Fat Tree Slice-Mix-AggRegaTe) algorithm. Concerning the innovative algorithm, the fat tree (FT) is introduced, the fat tree structure is adopted to optimize the data slicing scheme and the aggregation tree generation scheme, and the allocation of fixed time intervals is employed for nodes to reduce the transmission collision in the data aggregation process and to guarantee the completion of the data transmission. The simulation experiment results demonstrate that the FTSMART algorithm has presented the favorable performance in terms of the privacy protection, the network communication overhead, and the data aggregation accuracy.

IOT security privacy protection mechanism and mechanical structure design simulation optimization

Once the Internet of Things was proposed, it has received great attention from all walks of life and has become one of the top ten technologies that change the world. Therefore, more and more people are engaged in the research of the Internet of Things, after the unremitting efforts of all seniors. Now the Internet of Things has been applied to every aspect of our lives. However, in the application process of the Internet of Things, the protection of personal privacy will undoubtedly be involved. If this problem is not effectively resolved, it will become a major obstacle to the development of the Internet of Things. At present, the research of fully homomorphic technology has attracted great attention from the cryptography community. You can directly calculate the encrypted text encryption to obtain the output and decrypt the output. The result is the same as the output of the unencrypted plain text. This article first comprehensively describes the solution to the privacy protection problem in the existing Internet of Things, and then proposes to apply the fully homomorphic technology to the Internet of Things to make the services provided by the network more secure. Through the analysis of the basic composition and architecture of the existing Internet of Things system, a privacy protection interaction model for the Internet of Things is established, which uses a completely homomorphic technology. On this basis, the algorithm for implementing simple homomorphic encryption is improved, and general homomorphic encryption theory is proposed for some security issues. After using this method to encrypt privacy, the success rate of cracking dropped by 24%.

Edge Learning

Machine Learning ( ML ) has demonstrated great promise in various fields, e.g., self-driving, smart city, which are fundamentally altering the way individuals and organizations live, work, and interact. Traditional centralized learning frameworks require uploading all training data from different sources to a remote data server, which incurs significant communication overhead, service latency, and privacy issues. To further extend the frontiers of the learning paradigm, a new learning concept, namely, Edge Learning ( EL ) is emerging. It is complementary to the cloud-based methods for big data analytics by enabling distributed edge nodes to cooperatively training models and conduct inferences with their locally cached data. To explore the new characteristics and potential prospects of EL, we conduct a comprehensive survey of the recent research efforts on EL. Specifically, we first introduce the background and motivation. We then discuss the challenging issues in EL from the aspects of data, computation, and communication. Furthermore, we provide an overview of the enabling technologies for EL, including model training, inference, security guarantee, privacy protection, and incentive mechanism. Finally, we discuss future research opportunities on EL. We believe that this survey will provide a comprehensive overview of EL and stimulate fruitful future research in this field.

A Privacy Protection Method of Lightweight Nodes in Blockchain

Aiming at the privacy protection of lightweight nodes based on Bloom filters in blockchain, this paper proposes a new privacy protection method. Considering the superimposition effect of query information, node and Bloom filter are regarded as the two parties of the game. A privacy protection mechanism based on the mixed strategy Nash equilibrium is proposed to judge the information query. On this basis, a Bloom filter privacy protection algorithm is proposed when the probability of information query and privacy, not being leaked, is less than the node privacy protection. It is based on variable factor disturbance, adjusting the number of bits’ set to 1 in the Bloom filter to improve the privacy protection performance in different scenarios. The experiment uses Bitcoin transaction data from 2009 to 2019 as the test data to verify the effectiveness, reliability, and superiority of the method.

EDEN

Crowd sensing applications have demonstrated their usefulness in many real-life scenarios (e.g., air quality monitoring, traffic and noise monitoring). Preserving the privacy of crowd sensing app users is becoming increasingly important as the collected geo-located data may reveal sensitive information about these users (e.g., home, work places, political, religious, sexual preferences). In this context, a large variety of Location Privacy Protection Mechanisms (LPPMs) have been proposed. However, each LPPM comes with a given set of configuration parameters. The value of these parameters impacts not only the privacy level but also the utility of the resulting data. Choosing the right LPPM and the right configuration for reaching a satisfactory privacy vs. utility tradeoff is generally a difficult problem mobile app developers have to face. Solving this problem is commonly done by relying on a trusted proxy server to which raw geo-located traces are sent and privacy vs. utility assessment is performed enabling the selection of the best LPPM for each trace. In this paper we present EDEN, the first solution that selects automatically the best LPPM and its corresponding configuration without sending raw geo-located traces outside the user's device. We reach this objective by relying on a federated learning approach. The evaluation of EDEN on five real-world mobility datasets shows that EDEN outperforms state-of-the-art LPPMs reaching a better privacy vs. utility tradeoff.

A Theoretical Foundation and A Mechanism Construction of Credit-breaking Punishment

General office of the State Council (12.2020) “Guiding opinions on further perfecting the system of credit-breaking restraint and constructing a long-term mechanism of credit system” requirements: According to the general idea of law & regulation, protection of the legitimate rights and interests, prudence and moderation, list management, standardize and improve the mechanism of credit-breaking punishment and credit repair, promote the social credit system into a new stage of high-quality development. This paper explores the theoretical basis of credit restraint in the perspective of economic theory, analyses the types and effective elements of credit-breaking punishment from the perspective of mechanism building, summarizes the problems existing in privacy protection and credit repair mechanism of credit-breaking punishment measures, puts forward some suggestions on the formulation of social credit information catalogue & credit-breaking punishment measures list.