Anonymous Mechanism Research Articles

Multivariate Microaggregation of Set-Valued Data

Data controllers manage immense data, and occasionally, it is released publically to help the researchers toconduct their studies. However, this publically shared data may hold personally identifiable information (PII)that can be collected to re-identify a person. Therefore, an effective anonymization mechanism is required toanonymize such data before it is released publically. Microaggregation is one of the Statistical Disclosure Control (SDC) methods that are widely used by many researchers. This method adapts the k-anonymity principle togenerate k-indistinguishable records in the same clusters to preserve the privacy of the individuals. However,in these methods, the size of the clusters is fixed (i.e., k records), and the clusters generated through these methods may hold non-homogeneous records. By considering these issues, we propose an adaptive size clusteringtechnique that aggregates homogeneous records in similar clusters, and the size of the clusters is determinedafter the semantic analysis of the records. To achieve this, we extend the MDAV microaggregation algorithm tosemantically analyze the unstructured records by relying on the taxonomic databases (i.e., WordNet), and thenaggregating them in homogeneous clusters. Furthermore, we propose a distance measure that determines theextent to which the records differ from each other, and based on this, homogeneous adaptive clusters are constructed. In experiments, we measured the cohesiveness of the clusters in order to gauge the homogeneity ofrecords. In addition, a method is proposed to measure information loss caused by the redaction method. In experiments, the results show that the proposed mechanism outperforms the existing state-of-the-art solutions.

Minimax optimal goodness-of-fit testing for densities and multinomials under a local differential privacy constraint

Finding anonymization mechanisms to protect personal data is at the heart of recent machine learning research. Here, we consider the consequences of local differential privacy constraints on goodness-of-fit testing, that is, the statistical problem assessing whether sample points are generated from a fixed density f0, or not. The observations are kept hidden and replaced by a stochastic transformation satisfying the local differential privacy constraint. In this setting, we propose a testing procedure which is based on an estimation of the quadratic distance between the density f of the unobserved samples and f0. We establish an upper bound on the separation distance associated with this test, and a matching lower bound on the minimax separation rates of testing under non-interactive privacy in the case that f0 is uniform, in discrete and continuous settings. To the best of our knowledge, we provide the first minimax optimal test and associated private transformation under a local differential privacy constraint over Besov balls in the continuous setting, quantifying the price to pay for data privacy. We also present a test that is adaptive to the smoothness parameter of the unknown density and remains minimax optimal up to a logarithmic factor. Finally, we note that our results can be translated to the discrete case, where the treatment of probability vectors is shown to be equivalent to that of piecewise constant densities in our setting. That is why we work with a unified setting for both the continuous and the discrete cases.

Toward Privacy Preservation Using Clustering Based Anonymization: Recent Advances and Future Research Outlook

With the continuous increase in avenues of personal data generation, privacy protection has become a hot research topic resulting in various proposed mechanisms to address this social issue. The main technical solutions for guaranteeing a user’s privacy are encryption, pseudonymization, anonymization, differential privacy (DP), and obfuscation. Despite the success of other solutions, anonymization has been widely used in commercial settings for privacy preservation because of its algorithmic simplicity and low computing overhead. It facilitates unconstrained analysis of published data that DP and the other latest techniques cannot offer, and it is a mainstream solution for responsible data science. In this paper, we present a comprehensive analysis of clustering-based anonymization mechanisms (CAMs) that have been recently proposed to preserve both privacy and utility in data publishing. We systematically categorize the existing CAMs based on heterogeneous types of data (tables, graphs, matrixes, etc.), and we present an up-to-date, extensive review of existing CAMs and the metrics used for their evaluation. We discuss the superiority and effectiveness of CAMs over traditional anonymization mechanisms. We highlight the significance of CAMs in different computing paradigms, such as social networks, the internet of things, cloud computing, AI, and location-based systems with regard to privacy preservation. Furthermore, we present various proposed representative CAMs that compromise individual privacy, rather than safeguarding it. Finally, we discuss the technical challenges of applying CAMs, and we suggest promising opportunities for future research. To the best of our knowledge, this is the first work to systematically cover current CAMs involving different data types and computing paradigms.

Protecting Location Privacy by Multiquery: A Dynamic Bayesian Game Theoretic Approach

When using location-based services (LBSs), a user obtains points-of-interest (PoI) information by providing the LBS platform with his current geo-location. Such a search leads to potential privacy leakage if an adversary has access to his geo-data. Traditional <i>k</i>-anonymity mechanisms instruct a user to bear the overhead to report his current location together with <i>k</i> - 1 dummy locations to confuse the adversary, which only work well given a large number <i>k</i>. Aware of the common practices that a user is actually flexible in service requirement (e.g., as long as the searched PoIs are within his walking distance), we propose a novel approach to help the user gain location privacy from service flexibility for the challenging case of a small number <i>k</i>. By analyzing the strategic interaction between the user and the adversary in a dynamic Bayesian game, we prove that the user&#x2019;s equilibrium strategy depends on the adversary&#x2019;s capability of accessing geo-data. Take <i>k</i> = 2 for example, we find that if the adversary is not likely to access both geo-data, the user should report the two dummy locations at two different directions of his real location, and otherwise at the same direction. Perhaps surprisingly, the user may benefit from the adversary&#x2019;s access to more geo-data. Furthermore, we extend the game-theoretic approach for multi-query and arbitrary user location distributions. Numerical results show that our approach obviously outperforms <i>k</i>-anonymity mechanisms especially under a small number <i>k</i>.

A Conditional Privacy-Preserving Certificateless Aggregate Signature Scheme in the Standard Model for VANETs

Vehicular ad hoc networks (VANETs) are the communication foundation for future intelligent transportation systems and guarantee safe driving of intelligent networked vehicles. Moreover, VANETs face a series of security challenges related to the protection of vehicle privacy, authenticity of transmitted information, and bandwidth limitations. To resolve these contradictions, many certificateless aggregate signature (CLAS) schemes have been proposed. However, the majority of them can neither resist malicious-but-passive key generation center attacks, replay attacks, and link attacks, nor track the actual identities of malicious vehicles. Meanwhile, the security of previous CLAS schemes in VANETs is only formally provided in the random oracle model (ROM), which might be insecure in actual implementation. In addition, most CLAS schemes still have problems of large verification delays and high communication overhead. To address the above-mentioned problems, a new conditional privacy-preserving CLAS scheme in VANETs is proposed, which adopts full aggregation technology to reduce computation and bandwidth resources. According to the formal security proofs under the computational Diffie-Hellman problem (CDHP) given in the standard model (SM), the security level of this scheme is higher than that of other CLAS schemes under ROM in practical applications. Additionally, the use of pseudonym mechanism realizes conditional privacy protection in VANETs. The performance analysis shows that this scheme has a higher efficiency in terms of computation and communication overhead compared with several previous CLAS schemes.

A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.

Cross-Platform Strong Privacy Protection Mechanism for Review Publication

As a review system, the Crowd-Sourced Local Businesses Service System (CSLBSS) allows users to publicly publish reviews for businesses that include display name, avatar, and review content. While these reviews can maintain the business reputation and provide valuable references for others, the adversary also can legitimately obtain the user’s display name and a large number of historical reviews. For this problem, we show that the adversary can launch connecting user identities attack (CUIA) and statistical inference attack (SIA) to obtain user privacy by exploiting the acquired display names and historical reviews. However, the existing methods based on anonymity and suppressing reviews cannot resist these two attacks. Also, suppressing reviews may result in some reiews with the higher usefulness not being published. To solve these problems, we propose a cross-platform strong privacy protection mechanism (CSPPM) based on the partial publication and the complete anonymity mechanism. In CSPPM, based on the consistency between the user score and the business score, we propose a partial publication mechanism to publish reviews with the higher usefulness of review and filter false or untrue reviews. It ensures that our mechanism does not suppress reviews with the higher usefulness of reviews and improves system utility. We also propose a complete anonymity mechanism to anonymize the display name and avatars of reviews that are publicly published. It ensures that the adversary cannot obtain user privacy through CUIA and SIA. Finally, we evaluate CSPPM from both theoretical and experimental aspects. The results show that it can resist CUIA and SIA and improve system utility.

AWAP: Adaptive weighted attribute propagation enhanced community detection model for bitcoin de-anonymization

Bitcoin is a kind of decentralized cryptocurrency and widely used in online payment partially for its anonymity mechanism. The anonymity, however, also attracts the usage of cryptocurrency by criminals in ransomware and money laundering, and limits its further application and development. In this paper, we aim to improve Bitcoin’s auditability with de-anonymization. Many previous studies have used heuristic clustering or supervised machine learning to analyze the historical transactions for identifying user behaviors. However, heuristic clustering only considers the topological structure of the transaction graph and ignores the transaction attributes. While supervised learning is usually limited by the size of labeled datasets, resulting in an unsatisfactory accuracy. To resolve the above problems, we propose an Adaptive Weighted Attribute Propagation enhanced community detection model, named AWAP, which considers both the transaction’s topological structure and the transaction attributes. We first parse the transaction data from the public ledger and construct a bipartite graph to describe correlations between addresses and transactions. Then, we use a five-step feature engineering pipeline to extract Bitcoin address attributes and build an attribute graph. Finally, we design an adaptive weighted attribute propagation algorithm running on the attribute graph to classify the Bitcoin addresses and identify user behaviors. Extensive experiments highlight that AWAP model achieves 12% higher accuracy and 25% higher F-score on average, compared to the state-of-the-art Bitcoin address classifiers and other community detection algorithms. To evaluate the effectiveness of AWAP, we also present two case studies on Bitcoin address classification and Bitcoin trace-ability in ransomware.

α-MON: Traffic Anonymizer for Passive Monitoring

Packet measurements at scale are essential for several applications, such as cyber-security, accounting and troubleshooting. They, however, threaten users’ privacy by exposing sensitive information. Anonymization has been the answer to this challenge, i.e., replacing sensitive information with obfuscated copies. Anonymization of packet traces, however, comes with some challenges and drawbacks. First, it reduces the value of data. Second, it requires to consider diverse protocols because information may leak from many non-encrypted fields. Third, it must be performed at high speeds directly at the monitor, to prevent private data from leaking, calling for real-time solutions. We present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON, a flexible tool for privacy-preserving packet monitoring. It replicates input packet streams to different consumers while anonymizing protocol fields according to flexible policies that cover all protocol layers. Beside classic anonymization mechanisms such as IP address obfuscation, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON supports <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${z}$ </tex-math></inline-formula> -anonymization, a novel solution to obfuscate rare values that can be uniquely traced back to limited sets of users. Differently from classic anonymization approaches, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z-anonymity</i> works on a streaming fashion, with zero delay, operating at high-speed links on a packet-by-packet basis. We quantify the impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">z-anonymity</i> on traffic measurements, finding that it introduces minimal error when it comes to finding heavy-hitter services. We evaluate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON performance using packet traces collected from an ISP network and show that it achieves a sustainable rate of 40 Gbit/s on a Commercial Off-the Shelf server. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -MON is available to the community as an open-source project.

Blockchain-assisted handover authentication for intelligent telehealth in multi-server edge computing environment

Abstract Intelligent telehealth system (ITS) provides patients and medical institutions with a lot of convenience, medical institutions can achieve medical services for patients in time through monitored health data. However, as the scope of people's daily activities extends, the traditional single-server architecture is no longer applicable. To deal with this problem, a multi-server architecture has been proposed recently while there remains security and privacy challenges, including handover authentication. In this paper, we investigate a blockchain-assisted handover authentication and key agreement scheme for ITS in a multi-server edge computing environment. Specifically, we first propose a novel handover authentication model of ITS with multi-server edge computing architecture. Second, the proposed handover authentication scheme allows the authenticated server to assist users subsequently authenticate with other server, thereby achieving interactions with the server anytime and anywhere with low overhead. Finally, blockchain technology and strong anonymity mechanism are introduced to protect users’ privacy strictly. To our best knowledge, the proposed scheme is the first in the literature to provide efficient authentication, strict anonymity and computational load transfer simultaneously. The security analysis and performance evaluation show that our scheme can not only satisfy the security requirements but also achieve higher efficiency in computation and communication cost.

Pattern Mining and Detection of Malicious SQL Queries on Anonymization Mechanism

With the striking development of big data, individual privacy and data security obtain unprecedented importance. Database anonymization mechanism is created for protecting individual privacy by adding noise to the result of a query, which finds a tradeoff between the privacy and utility of personal data. However, corresponding attacks are emerging continuously resulting in a high risk of individual identification. In this paper, we learn patterns of malicious SQL queries and propose a novel detection method. Association rules are used to mine patterns and features of noise-exploitation attacks, and parse trees are applied to the feature extraction of SQL, thereby we construct feature vectors and input them into the classifiers. At the same time, we also propose a SQL generation method to generate query samples based on a real database for model training and testing. Experiments show that our detection method can significantly prevent noise-exploitation attacks including almost all differential attacks and 91% cloning attacks based on the synthetic dataset, which ensures a strong degree of data utility.

Privacy in the Cloud: A Survey of Existing Solutions and Research Challenges

Private data is transmitted and stored online every second. Therefore, security and privacy assurances should be provided at all times. However, that is not always the case. Private information is often unwillingly collected, sold, or exposed, depriving data owners of their rightful privacy. In this article, various privacy threats, concepts, regulations, and personal data types are analyzed. An overview of Privacy Enhancing Technologies (PETs) and a survey of anonymization mechanisms, privacy tools, models, and metrics are presented together with an analysis of respective characteristics and capabilities. Moreover, this article analyses the applicability of the reviewed privacy mechanisms on today's Cloud Services and identifies the current research challenges to achieve higher privacy levels in the Cloud.

Efficient and Privacy-Preserving Medical Research Support Platform Against COVID-19: A Blockchain-Based Approach

COVID-19 is a major global public health challenge and difficult to control in a short time completely. To prevent the COVID-19 epidemic from continuing to worsen, global scientific research institutions have actively carried out studies on COVID-19, thereby effectively improving the prevention, monitoring, tracking, control, and treatment of the epidemic. However, the COVID-19 electronic medical records (CEMRs) among hospitals worldwide are managed independently. With privacy consideration, CEMRs cannot be made public or shared, which is not conducive to in-depth and extensive research on COVID-19 by medical research institutions. In addition, even if new research results are developed, the disclosure and sharing process is slow. To address this issue, we propose a blockchain-based medical research support platform, which can provide efficient and privacy-preserving data sharing against COVID-19. First, hospitals and medical research institutions are treated as nodes on the alliance chain, so consensus and data sharing among the nodes is achieved. Then, COVID-19 patients, doctors, and researchers need to be authenticated in various institutes. Moreover, doctors and researchers need to be registered with the Fabric certificate authority. The CEMRs for COVID-19 patients uses the blockchain's pseudonym mechanism to protect privacy. After that, doctors upload CEMRs on the alliance chain, and researchers can obtain CEMRs from the alliance chain for research. Finally, the research results will be published on the blockchain for doctors to use. The experimental results show that the read and write performance and security performance on the alliance chain meet the requirements, which can promote the wide application of scientific research results against COVID-19.

Implications of Data Anonymization on the Statistical Evidence of Disparity

Research and practical development of data anonymization techniques has proliferated in recent years. Although the privacy literature has questioned the efficacy of data anonymization at protecting individuals against harms associated with re-identification, this paper raises another new set of questions: whether anonymization techniques themselves can mask statistical disparities and thus conceal evidence of disparate impact that is potentially discriminatory. If so, the choice of data anonymization technique to protect privacy, and the specific technique employed, may pick winners and losers. Examining the implications of these choices on the potentially disparate impact of privacy protection on underprivileged sub-populations is thus a critically important policy question. The paper begins with an interdisciplinary overview of common mechanisms of data anonymization and prevalent types of statistical evidence for disparity. In terms of data-anonymization mechanisms, the common ones are data removal (e.g., k-anonymity), which aims to remove the part of a dataset that could potentially identify an individual; and noise insertion (e.g., differential privacy), which inserts into a dataset carefully designed noises that block the identification of individuals yet allow the accurate recovery of certain summary statistics. In terms of the statistical evidence for disparity, the commonly accepted types are disparity through separation (e.g., the two or three standard deviations rule for a prima facie case of discrimination), which is grounded in the idea of detecting the separation between the outcome distributions for different sub-populations; and disparity through variation (e.g., the likely than not rule in toxic tort cases), which concentrates on the magnitude of difference between the mean outcomes of different sub-populations. We develop conceptual foundation and mathematical formalism demonstrating that the data anonymization mechanisms have distinctive impacts on the identifiability of disparity, which also varies based on its statistical operationalization. Specifically, under the regime of disparity through separation, data removal tends to produce more false positives (i.e., detecting false disparity when none exists) than false negatives (i.e., failing to detect an existing disparity); while noise insertion rarely produces any false positives at all. Meanwhile, noise insertion does produce false positives (equally likely as false negatives) under the regime of disparity through variation; while the likelihood for data removal to produce false positives and false negatives depend on the underlying data distribution. We empirically validated our findings with an inpatient dataset from one of the five most populated states in the U.S. We examined four data-anonymization techniques (two in the data-removal category and the other in noise insertion), ranging from the current rules used by the State of Texas to anonymize their state-wide inpatient discharge dataset to the state-of-the-art differential privacy algorithms for regression analysis. After presenting the empirical results, which confirmed our conceptual and mathematical findings, we conclude the paper by discussing the business and policy implications of these findings, highlighting the need for firms and policy makers to balance between the protection of privacy and the recognition/rectification of disparate impact. In sum, our paper identifies an important knowledge gap in both tech and law fields: whether data anonymization technologies themselves can mask statistical disparities and thus conceal the evidence of disparate impact that is potentially discriminatory. The emergence of privacy laws (e.g., GDPR) gives primacy to answering this question, because if such disparate impacts do exist, legislators and regulators would be essentially picking winners and losers by requiring or incentivizing the use of data anonymization techniques. This paper tackles this timely yet complex challenge, especially given the current public discourse in the U.S. about racial discrimination, and the worldwide trend of prioritizing the protection of consumer privacy in legislations and regulations.

Impact of prior knowledge on privacy leakage in trajectory data publishing

The rapid growth in the usage of location-based services has resulted in extensive research on users’ trajectory data publishing. But, a key concern here is a potential breach of user privacy through various linkage attacks by an efficient adversary. There exist a few privacy preservation methods to defend against either single or combination of linkage attacks, namely Identity linkage attack, Attribute linkage attack, and Similarity attack. However, the Correlated-records linkage attack has not been studied in any previous privacy preservation methods, and there is no privacy preservation method to address all the above four linkage attacks. In this paper, a novel anonymization method is proposed to provide the privacy guarantee to users against all the four linkage attacks. The proposed method consists of two phases, namely virtualization and suppression. The virtualization method works as a replacement mechanism for the sensitive attribute and the suppression method works as anonymization mechanism for users trajectories, in order to anonymize the trajectory datasets for preserving users’ privacy from the above four linkage attacks. To validate the efficiency of the proposed method, it is also compared with existing methods, namely KCL-L, KCL-G and KCL-PPTD, considering both synthetic and real-time datasets. The experimental results exhibit that the proposed approach results in better performance with a significant reduction in the information loss when compared to other states of the art methods.

An efficient blockchain-based privacy preserving scheme for vehicular social networks

In vehicular social networks (VSNs), edge stations or cloud service provider can support the traffic services or location services to vehicles. Moreover, the information sharing among vehicles can assist vehicles to avoid traffic accidents and guarantee driving safety. However, it is easy to be threatened in the communication of vehicle-to-edge station, vehicle-to-cloud service provider and vehicle-to-vehicle in VSNs, which leads to the vehicle’s privacy leakage easily. Besides, some malicious users may provide untrustworthy information to mislead others just for its selfishness. Hence, in this paper, we propose an efficient, reliable and privacy-preserving scheme based on blockchain for VSNs. In our scheme, pseudonym mechanism is employed to achieve individual anonymization by concealing the vehicles’ identity. Moreover, to encourage vehicles to report trustworthy information, incentive-punishment mechanism is proposed. Meanwhile, we propose multi-factors and single-factor weight-based evaluation mechanism to evaluate the reliability of message. Additionally, Practical Byzantine Fault Tolerance (PBFT) and blockchain are also employed to achieve consensus and store the records respectively, which can prevent malicious entities from manipulating on vehicles’ reward scores and credit scores. Finally, we analyze the security of the proposed scheme in terms of external attacks, internal attacks, collusion attacks etc. The relevant experimental results are shown that our scheme is feasible and efficient.

Efficient Certificateless Aggregate Signature With Conditional Privacy Preservation in IoV

As an extension of traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) enables information collection and dissemination, which brings a lot of convenience and benefits to the intelligent transportation systems. However, the booming IoV confronts a few challenges in the aspects of vehicle location privacy preservation and the authenticity of the transmitted information. In order to meet these challenges, we propose an efficient certificateless aggregate signature scheme with conditional privacy preservation in this article. Our scheme utilizes the technique of full aggregation to reduce the bandwidth resources and computing overhead. Besides, the conditional privacy preservation in IoV system is realized by using pseudonym mechanism. We demonstrate that the proposed scheme is secure against the Type-I and Type-II adversaries in the random oracle under the computational Diffie–Hellman assumption. In addition, the proposed scheme is compared with related works from aspects of computation cost, communication efficiency, and security requirements. The comparison results show that the proposed scheme is efficient, and it is suitable for resource-constrained environments.

Mutual authentication‐based RA scheme for embedded systems

To improve the security and efficiency of remote attestation (RA) for embedded systems, this study proposes mutual authentication-based RA scheme for embedded systems. Especially, the authors design an RA framework based on authentication agents and measurement agents, which combines the mutually anonymous identity authentication scheme with the platform integrity attestation. During the identity authentication period, based on the traditional direct anonymous authentication scheme, the time-stamping mechanism and the mutual direct anonymous attestation mechanism are proposed to achieve bidirectional anonymous authentication of both parties in the communication. During the platform integrity attestation period, combining with the locality principle, they improve the data structure for storing the integrity measurements of the module and propose an RA mechanism based on locality principle-based hash tree. This mechanism can shorten the length of the certification path and improve the verification efficiency of platform configuration integrity certification. Furthermore, experimental results and analysis show that the efficiency of the proposed scheme is superior to the existing schemes.

Identifying the vulnerabilities of bitcoin anonymous mechanism based on address clustering

The anonymity mechanism of bitcoin is favored by the society, which promotes its usage and development. An adversary should not be able to discover the relation between bitcoin addresses and bitcoin users to ensure effective privacy. However, the relation among bitcoin transactions can be used to analyze the bitcoin privacy information, which seriously jeopardizes the bitcoin anonymity. Herein, we describe the vulnerabilities associated with the anonymity mechanism of bitcoin, including the relation among bitcoin addresses and the relation among bitcoin users. Further, we demonstrate that the existing methods do not guarantee the comprehensiveness, accuracy, and efficiency of the analysis results. We propose a heuristic clustering method to judge the relation among bitcoin addresses and employ the Louvain method to discover the relation among bitcoin users. Subsequently, we construct an address-associated database of historical transactions and implement real-time updates. Extensive experiments are used to demonstrate the comprehensiveness, accuracy, and efficiency of the proposed scheme. Specifically, the proposed scheme reveals the privacy vulnerability associated with the blockchain technology. We expect that our scheme can be applied to improve the blockchain technology.

Privacy Preserving in LBS resilient to Location Injection Attacks

Location-based services have become indispensable in people's life with expeditious development of technology. Location-based services(LBS) refers to the services provided by the LBS servers with regards to area and point of interest. Alternatively, the LBS means getting the right information at the right place in time. Protecting user location privacy is the most challenging factor in LBS. This survey aims to present various mechanisms in preserving the user's location privacy and proposes a mechanism for preserving the privacy of user location and query against the location injection attacks. We will be discussing credibility based k- anonymity mechanism for preserving the location of the user and homomorphic encryption for preserving the query of the user resilient location injection attacks in this paper.