Privacy Protection Issues Research Articles

The impact of interactive dependence on privacy protection behavior based on evolutionary game

With the rapid development of social networks, privacy protection has become a hot issue in the field of information security. Here we introduce the framework of evolutionary game theory to explore the issue of privacy protection in social networks. Since reciprocity is widely present in social activities, we introduce the heterogeneous interaction mode, in which players can adopt different strategies for different opponents. In addition, the parameter u is introduced to measure the player's dependence on the opponent who interacts directly with the central individual during the update strategy phase. Here, we explore the impact of heterogeneous interaction dependency strength on privacy protection. A series of computer simulation results suggest that heterogeneous decision behavior can promote privacy protection, and there exists an optimal dependence strength interval for the group to achieve a higher level of privacy protection. Certainly, larger reciprocity strength and smaller cost can significantly increase the privacy protection behavior. Our research work is of great significance in solving the long-term safe and effective development of social networks.

Privacy‐preserving data aggregation scheme for edge computing supported vehicular ad hoc networks

AbstractVehicular ad hoc networks (VANETs) allow vehicles getting road situation by connecting with roadside unit (RSU) through the on‐board unit, and this information can be processed to improve transportation services in a timely manner. Meanwhile, the three‐tier edge computing system can support the VANETs well. However, the limited bandwidth of the VANETs and the privacy of uploaded data are still issues to be resolved. Data aggregation is an effective way to address the issue of limited bandwidth and data privacy protection for individual devices. In this article, based on the bilinear pairing and Paillier homomorphic encryption, we design a privacy‐preserving data aggregation scheme for edge computing supported VANETs, which not only preserves the privacy of uploaded data but also realizes batch operations. Also, the proposed scheme guarantees the features of data integrity and source authentication. Moreover, the comparison of performance evaluation between ours and other recent schemes shows that the proposed scheme significantly reduces the communication overhead and is more suitable for resource‐constrained terminal device.

IOT security analysis of BDT-SVM multi-classification algorithm

With the continuous development of the Internet of Things, the issue of IoT data security and privacy protection has received increasing attention. Compared with traditional Internet applications, IoT applications using smart terminals as supporting technologies have more complicated and serious security problems. This paper proposes a multi-classification algorithm based on double support vector machine decision tree. For all samples, the samples with the most separability were divided into two categories according to the size of the distinguishability between classes. In these two sub-categories, the most divisible samples are separately searched and divided into two categories, so that they can not be subdivided. This paper proposes a location privacy security protection mechanism based on anonymous tree and box structure, which provides location privacy protection for services oriented to intelligent terminals. The simulation results show that the common sub-collection of the anonymous group is larger, and the time overhead of building the group is smaller. At the same time, the BDT-SVM multi-classification algorithm can improve the accuracy of the intrusion detection system and reduce the detection time.

Grover algorithm-based quantum homomorphic encryption ciphertext retrieval scheme in quantum cloud computing

Existing classical ciphertext retrieval schemes are mainly developed according to the homomorphic encryption that is a cryptographic technique based on computational complexity theory of mathematical puzzles. In quantum computing, on the one hand, most of traditional asymmetric encryption can be quickly cracked as the computational capacity of quantum computer is much higher than that of traditional digital computer. Hence, the quantum homomorphic encryption scheme is widely used in data encryption for the issue of privacy protection in quantum computing. But on the other the retrieval efficiency of homomorphic encrypted data is insufficient especially in quantum cloud computing. Therefore, this paper proposes a novel quantum homomorphic encryption ciphertext retrieval (QHECR) scheme basing on the Grover algorithm to solve the problem of homomorphic encrypted ciphertext data retrieval in quantum cloud computing. Our scheme is to improve such retrieval efficiency mentioned above where the interaction process is not required even if the T-gate exists in the circuit. In the experiment, two qubits without the T-gate are conducted on both simulations and real quantum devices by using IBM quantum information science kit (Qiskit). The results show that the proposed QHECR scheme is capable of achieving the retrieval encrypted data when the T-gate does not exist in the evaluation circuit in ciphertext environment. Moreover, a verification experiment about the T-gate key update algorithm is implemented to verify the feasibility and reliability of the proposed scheme in the Qiskit as well, which indicates that the QHECR is still available when the T-gate exists in the circuit. Since the decryption is inefficient when there are exponential T-gates in the circuit, our proposed scheme is suitable for low T-gate complexity.

Speaker Anonymization for Personal Information Protection Using Voice Conversion Techniques

As speech-based user interfaces integrated in the devices such as AI speakers become ubiquitous, a large amount of user voice data is being collected to enhance the accuracy of speech recognition systems. Since such voice data contain personal information that can endanger the privacy of users, the issue of privacy protection in the speech data has garnered increasing attention after the introduction of the General Data Protection Regulation in the EU, which implies that restrictions and safety measures for the use of speech data become essential. This study aims to filter the speaker-related voice biometrics present in speech data such as voice fingerprint without altering the linguistic content to preserve the usefulness of the data while protecting the privacy of users. To achieve this, we propose an algorithm that produces anonymized speeches by adopting many-to-many voice conversion techniques based on variational autoencoders (VAEs) and modifying the speaker identity vectors of the VAE input to anonymize the speech data. We validated the effectiveness of the proposed method by measuring the speaker-related information and the original linguistic information retained in the resultant speech, using an open source speaker recognizer and a deep neural network-based automatic speech recognizer, respectively. Using the proposed method, the speaker identification accuracy of the speech data was reduced to 0.1-9.2%, indicating successful anonymization, while the speech recognition accuracy was maintained as 78.2-81.3%.

A new fully homomorphic cryptosystem based on a super-increasing sequence

Cloud computing is a powerful computing paradigm that provides a variety of computing services to its users. An example is storage, which allows individuals and enterprises to outsource their files to remote storage. However, saving private information onto third-party storage increases the security issues of data and privacy protection concerns. For this reason, cloud service providers (CSPs) are required to save an encrypted version of user data. In this paper, a novel encryption technique based on the use of Fully Homomorphic Encryption is presented. The technique uses a superincreasing sequence to derive the key and works on encrypted data with no need for decryption; this yields the same results as performing it on plaintext data. In the proposed technique, the characters are converted to their corresponding ASCII code values, which differs from the binary values produced by other existing techniques.

Is There a ‘Right to Be Forgotten’ in China? Judicial Response to China’s Tort Law on Privacy and Data Protection Issues

Is There a ‘Right to Be Forgotten’ in China? Judicial Response to China’s Tort Law on Privacy and Data Protection Issues

A Discovery Method for New Words From Mobile Product Comments

A large number of new words in product reviews generated by mobile terminals are valuable indicators of the privacy preferences of customers. By clustering these privacy preferences, sufficient information can be collected to characterize users and provide a data basis for the research issues of privacy protection. The widespread use of mobile clients shortens the string length of the comment corpus generated by product reviews, resulting in a high repetition rate. Therefore, the effective and accurate recognition of new words is a problem that requires an urgent solution. Hence, in this paper, we propose a method for discovering new words from product comments based on Mutual Information and improved Branch Entropy. Firstly, by calculating the Co-occurrence Frequency and Mutual Information between words and adjacent words, the character strings of words after pre-processing and word segmentation are expanded left and right respectively to discover the potential word set. The candidate set of new words is obtained by means of an improved support filtering algorithm. Finally, a new word set is built by applying an improved Branch Entropy filtering algorithm and removing old words. The experimental results show that this method can accurately and effectively identify new words in product comments.

Different data futures: An experiment in citizen data

Big data and citizens are inseparable: from smartphones, meters, fridges and cars to internet platforms, the data of most digital technologies is the data of citizens. In addition to raising political and ethical issues of privacy, confidentiality and data protection, the repurposing of big data calls for rethinking relations to citizens in the production of official statistics if they are to be trusted. I argue for relations that involve co-producing data – or ‘citizen data’ – where citizens are engaged in statistical production, from the design of a data production platform to the interpretation and analysis of data. While raising issues such as data quality, I suggest that in a time of ‘alternative facts’, what constitutes legitimate knowledge and expertise are major political sites of contention and struggle and require going beyond defending existing practices towards inventing new ones. In this light, the future of official statistics not only depends on inventing new data sources and methods but also mobilizing the possibilities of digital technologies to establish new relations with citizens.

Enabling secure wireless multimedia resource pricing using consortium blockchains

Wireless Service Providers (WSPs) and Content Providers (CPs) charge common data price according to the flat rate, the transmission time, or the data usage, irrespective of the content characteristics. The multimedia compression parameters that determine the multimedia importance diversities among frames have largely been ignored in media pricing research. We establish a new economics-driven resource allocation framework for multimedia transmissions among WSPs, CPs, and End Users (EUs) based on Smart Media Pricing (SMP). The WSPs transmit media streams provided by CPs to the EUs to earn money. However, facing with the transaction security and privacy protection issues, we undertake promising consortium blockchain to improve resource pricing transaction security without reliance on a trusted third party. A novel Stackelberg game with consortium blockchain is firstly established to allocate transmission resources such as Transmission Times’ Limitations (TTLs) from an economic point of view. Then we investigate the utility maximization problems of the leader (WSP and CP) and the follower (EU) in terms of TTLs respectively. Finally, the optimal TTL and the optimal price are non-uniformly determined for each frame according to the game equilibria, by analyzing the payment from EUs, the channel conditions, and the media characteristics in terms of frame importance. Security analysis shows that our proposed scheme achieves better transaction security and privacy protection. Experimental comparison with uniform pricing based on equal TTL or equal frame importance validates the effectiveness of the proposed economic model.

Privacy and data protection in the surveillance society: The case of the Prüm system

The simultaneous localisation and globalisation of ‘terrorist threats' and cross-border criminality have led to increased expansion of surveillance activities and greater cross-border police and judicial cooperation, placing a greater priority on these activities within the political agenda of the EU. In this scenario, the expansion of technological systems for surveillance and monitoring, and the large-scale exchange of citizens’ personal data play a pivotal role in the “fight against crime”. This paper explores the multiplicity of data protection regimes in different EU Member States within the framework of the Prüm system. While EU regulations establish minimum standards for personal data flows at the transnational level, local and domestic practices are extremely heterogeneous.Based on analysis of 37 interviews conducted with professionals involved in the automated exchange of forensic genetic profiles, this paper provides empirical data that highlights the tensions between the local and the global within DNA data exchanges across the EU. These tensions relate to differentiated sociotechnical imaginaries regarding the protection of personal data flowing between Member-States. In sum, this paper analyses the potential threats to human rights created by the exchange of personal data with regards to issues of privacy and data protection.

Efficient Attribute-Based Encryption with Privacy-Preserving Key Generation and Its Application in Industrial Cloud

Due to the rapid development of new technologies such as cloud computing, Internet of Things (IoT), and mobile Internet, the data volumes are exploding. Particularly, in the industrial field, a large amount of data is generated every day. How to manage and use industrial Big Data primely is a thorny challenge for every industrial enterprise manager. As an emerging form of service, cloud computing technology provides a good solution. It receives more and more attention and support due to its flexible configuration, on-demand purchase, and easy maintenance. Using cloud technology, enterprises get rid of the heavy data management work and concentrate on their main business. Although cloud technology has many advantages, there are still many problems in terms of security and privacy. To protect the confidentiality of the data, the mainstream solution is encrypting data before uploading. In order to achieve flexible access control to encrypted data, attribute-based encryption (ABE) is an outstanding candidate. At present, more and more applications are using ABE to ensure data security. However, the privacy protection issues during the key generation phase are not considered in the current ABE systems. That is to say, the key generation center (KGC) knows both of attributes and corresponding keys of each user. This problem is especially serious in the industrial big data scenario, because it will cause great damage to the business secrets of industrial enterprises. In this paper, we design a new ABE scheme that protects user’s privacy during key issuing. In our new scheme, we separate the functionality of attribute auditing and key generating to ensure that the KGC cannot know user’s attributes and that the attribute auditing center (AAC) cannot obtain the user’s secret key. This is ideal for many privacy-sensitive scenarios, such as industrial big data scenario.

Real Money, Real Me: Life Told by Third-party Mobile Payment Platforms

Platforms are constantly shaping online identities. The third-party mobile payment solution is one of the latest technological innovations that have redefined how we lead our lives today. The popularity of third-party mobile payment in China allows platforms greater data power to present users’ identities either as individual consumers or as members of a consumption community. This article first introduces the background and data features of third-party mobile payment platforms. It then concentrates on the two dominant platforms, Alipay and WeChat Pay, to demonstrate their different monetary approaches to user identities, characterized respectively by personal finance and by collective belonging. Finally it will discuss in brief further implications, including the possibility of a unified on/offline identity, the identification of an online user, and the issue of privacy protection.

Concepts of Cloud Computing and Protection of Data in Cloud Computing

The internet has changed the world in a strong way.it has traveled from the concept of parallel computing to distributed computing to grid computing and recently to cloud computing. Cloud computing is a recent trend in Information Technology that moves computing and data away from desktop and portable personal computers into large data center. The main advantage of cloud computing is the user cannot pay for infrastructure, its installation, required man power to handle such infrastructure and maintenance. Cloud computing technology is collecting success stories of savings, ease of use, ease of access and increased flexibility in controlling how resources are used at any given time to deliver computing capability. Cloud providers who can demonstrate that they protect personal information may be more truthful and therefore more attractive to potential Cloud users. The cloud service can be implemented in three different service models, such as Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS). Data security and privacy protection issues are relevant to both hardware and software in the cloud architecture. This study is to review the concepts of cloud computing and different security techniques and protecting data in the cloud. Keywords : Cloud computing, Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), Infrastructure-as-a-Service (IaaS). DOI : 10.7176/CEIS/10-4-01 Publication date :May 31 st 2019

Citizen Data and Trust in Official Statistics

Many, if not most, big data are connected to the lives of citizens: their movements, opinions, and relations. Arguably big data and citizens are inseparable: from smartphones, meters, fridges and cars to internet platforms, the data of digital technologies is the data of citizens. In addition to raising political and ethical issues of privacy, confidentiality and data protection, this calls for rethinking relations to citizens in the production of data for statistics if they are to be trusted by citizens. We outline an approach that involves co-producing data, where citizens are engaged in all stages of statistical production, from the design of a data production platform to the interpretation and analysis of data. While raising issues such as data quality and reliability, we argue co-production can potentially mitigate problems associated with the re-purposing of big data. We argue that in a time of ‘alternative facts’, what constitutes legitimate knowledge and expertise are major political sites of contention and struggle and require going beyond defending existing practices towards inventing new ones. In this context, we argue that the future of official statistics not only depends on inventing new data sources and methods but also mobilising the possibilities of digital technologies to establish new relations with citizens.

De-identifying Unstructured Medical Text and Attribute-based Utility Measurement

De-identification is a method by which the remaining information can not be referred to a specific individual by removing the personal information from the data set. As a result, de-identification can lower the exposure risk of personal information that may occur in the process of collecting, processing, storing and distributing information. Although there have been many studies in de-identification algorithms, protection models, and etc., most of them are limited to structured data, and there are relatively few considerations on de-identification of unstructured data. Especially, in the medical field where the unstructured text is frequently used, many people simply remove all personally identifiable information in order to lower the exposure risk of personal information, while admitting the fact that the data utility is lowered accordingly. This study proposes a new method to perform de-identification by applying the k-anonymity protection model targeting unstructured text in the medical field in which de-identification is mandatory because privacy protection issues are more critical in comparison to other fields. Also, the goal of this study is to propose a new utility metric so that people can comprehend de-identified data set utility intuitively. Therefore, if the result of this research is applied to various industrial fields where unstructured text is used, we expect that we can increase the utility of the unstructured text which contains personal information.

Partially policy-hidden attribute-based broadcast encryption with secure delegation in edge computing

The rapid growth of data has successfully promoted the development of edge computing, which is used for processing the data at the edge of network. The emergence of edge computing compensates for the network delay caused by massive data uploads to the cloud. However, the issues of data security and privacy protection still need to be resolved. In this paper, we propose an efficient ciphertext-policy attribute-based encryption (CP-ABE) scheme that for the first time simultaneously achieves partially hidden policy, direct revocation, and verifiable outsourced decryption. Specifically, in our scheme, the concept of partially hidden policy is introduced to protect private information in an access policy. In addition, after a revocation is successfully executed, the revoked users will not be able to access the message without affecting any other non-revoked users. Our new scheme leverages the outsourcing technique to minimize the overhead required of the user. We demonstrate that our scheme is secure under the Decisional (q−1) Diffie–Hellman assumption and the Decisional Bilinear Diffie–Hellman assumption, as well as evaluating its performance using simulations.

Cloud-based RFID mutual authentication scheme for efficient privacy preserving in IoV

With the continuous development of comprehensive technologies in various fields, the cyber-physical systems has been successfully applied in many fields in a large scale. The privacy and security issues in the system have gradually become the focus of attention. This article focuses on privacy protection issues in the area of Internet of Vehicles (IoV). IoV has developed rapidly and come into public consciousness quickly. Radio Frequency Identification (RFID) technology is a safe and reliable sensor data processing system, which is widely used in IoV. However, RFID system often suffers some risks of privacy disclosure. For example the owners are reluctant to disclose their private information such as their precise location information to the public network. Faced with those security risks, it is of great importance for the RFID system applied to IoV to protect private information. In this paper, we propose a cloud-based mutual authentication protocol aiming at ensuring efficient privacy preserving in IoV system, which enables people the efficiently and intelligently travel mode while protecting their privacy from divulging. Moreover, that the anonymity of tag is implemented not only protects the privacy data of the owners, but also prevents the malicious tracking from outside attackers. As to the proposed scheme, the proof based on BAN logic indicates it is of logic security. Security analysis and performance evaluation show that the scheme can be a good security solution for IoV with the feature of good safety and reliability.

Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

Vehicular Ad-Hoc Networks (VANETs) have received a great attention recently due to their potential and various applications. However, the initial phase of the VANET has many research challenges that need to be addressed, such as the issues of security and privacy protection caused by the openness of wireless communication networks among the city-wide applied regions. Specially, anomaly detection for a VANET has become a challenging problem, due to the changes in the scenario of VANETs comparing with traditional wireless networks. Motivated by this issue, we focus on the problem of anomaly detection in VANETs, and propose an effective anomaly detection approach based on the convolutional neural network in this paper. The proposed approach takes into account the spatio-temporal and sparse features of VANET traffic, and it uses a convolutional neural network architecture and a loss function based on Mahalanobis distance to extract and estimate the traffic matrix. Then, reinforcement learning is used to implement anomaly detection. Furthermore, a comprehensive assessment is provided to validate the proposed approach, which illustrates the effectiveness of this approach.

Research on the Association of Mobile Social Network Users Privacy Information Based on Big Data Analysis

The issue of privacy protection for mobile social networks is a frontier topic in the field of social network applications. The existing researches on user privacy protection in mobile social network mainly focus on privacy p... | Find, read and cite all the research you need on Tech Science Press