Anonymity Model Research Articles

A Utility-Aware Anonymization Model for Multiple Sensitive Attributes Based on Association Concealment

A Utility-Aware Anonymization Model for Multiple Sensitive Attributes Based on Association Concealment

Statistical privacy protection for secure data access control in cloud

Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.

Cluster-based anonymity model and algorithm for 1:1 dataset with a single sensitive attribute using machine learning technique

Privacy is a significant issue that requires consideration in all applications. Data collected from various individuals and organizations must be disclosed to the public or private parties for analysis and research purposes. The collected data are studied and analyzed digitally for the extraction of various useful patterns for decision-making research purposes. Privacy-preserving data publishing is significant as privacy violations in the patient’s data may have an adverse effect on the individual positive reputation. An efficient Cluster Based anonymity model has been proposed to anonymizes the 1:1 dataset with a single sensitive attribute through the introduction of a concept named “Semi-sensitive attribute.” Based on correlation, the attributes are categorized as quasi-identifier and semi-sensitive attributes. The k-anonymity is implemented on the quasi-identifier with the semi-sensitive attribute table and Fuzzy c-means clustering has been implemented to fix a range of values for anonymizing the semi-sensitive attributes. The disease is considered a sensitive attribute as the research work focuses on the medical dataset. The proposed model is demonstrated to resist the three privacy attacks such as, i)Identity Disclosure, ii) Attribute Disclosure, and iii) Membership Disclosure. The utility loss is calculated for each row and utility loss of each record are aggregated and considered as the total information loss for each attribute. Cluster Based anonymity model measured the utility loss for all the attributes and the average utility loss for the anonymized patient dataset is 3.78%.

An enhanced discrete particle swarm optimization for structural k-Anonymity in social networks

Recently, social network usage has exhibited explosive growth, leading to a huge amount of users’ private data available. The main challenge in releasing social network data publicly is the protection of the users’ privacy while preserving its utility for third parties. Accordingly, several social network privacy-preserving methods have been introduced, where anonymization is the most common approach. Structural k-anonymity is a widely used anonymization model to mask the structure of social networks by clustering the edges and nodes into super-edges and super-nodes. However, it comes at the cost of losing structural information, which is measured by a criterion called structural information loss (SIL). This study introduces an enhanced discrete particle swarm optimization (EDPSO) algorithm, which effectively minimizes the SIL within the clustering process of the structural k-anonymity model, leading to a high-utility anonymized network. In this regard, we propose a vector-based solution representation that can be efficiently exploited by the EDPSO. Moreover, a novel position updating heuristic is suggested for the EDPSO, which adaptively tunes the operators’ selection probabilities. This happens based on each operator’s performance both in the current iteration and their history regarding the number and the average amount of fitness improvements in the previous iterations. We also propose two fortified versions of the EDPSO algorithm (EDPSOVNS and EDPSOSA) by employing two new network-specific local search strategies to enhance the exploration, exploitation, and convergence rate of the process. Simulation results on the nine real-world networks demonstrate the superiority of the suggested algorithms in terms of the fitness value, reliability, and convergence rate over other analyzed approaches found in the literature.

EPA-GAN: Electric Power Anonymization via Generative Adversarial Network Model

The contemporary landscape of electricity marketing data utilization is characterized by increased openness, heightened data circulation, and more intricate interaction contexts. Throughout the entire lifecycle of data, the persistent threat of leakage is ever-present. In this study, we introduce a novel electricity data anonymization model, termed EPA-GAN, which relies on table generation. In comparison to existing methodologies, our model extends the foundation of generative adversarial networks by incorporating feature encoders and feedback mechanisms. This adaptation enables the generation of anonymized data with heightened practicality and similarity to the original data, specifically tailored for mixed data types, thereby achieving a deliberate decoupling from the source data. Our proposed approach initiates by parsing the original JSON file, encoding it based on variable types and features using distinct feature encoders. Subsequently, a generative adversarial network, enhanced with information, downstream, generator losses, and the Was + GP modification, is employed to generate anonymized data. The introduction of random noise fortifies privacy protection during the data generation process. Experimental validation attests to a conspicuous reduction in both machine learning utility and statistical dissimilarity between the data synthesized by our proposed anonymization model and the original dataset. This substantiates the model’s efficacy in replacing the original data for mining analysis and data sharing, thereby effectively safeguarding the privacy of the source data.

Designing an Attribute-Based Encryption Scheme with an Enhanced Anonymity Model for Privacy Protection in E-Health

Designing an Attribute-Based Encryption Scheme with an Enhanced Anonymity Model for Privacy Protection in E-Health

Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

Reciprocal Bucketization (RB) - An Efficient Data Anonymization Model for Smart Hospital Data Publishing

Tor network anonymity evaluation based on node anonymity

In order to address the shortcomings of traditional anonymity network anonymity evaluation methods, which only analyze from the perspective of the overall network and ignore the attributes of individual nodes, we proposes a dynamic anonymity model based on a self-built anonymous system that combines node attributes, network behavior, and program security monitoring. The anonymity of evaluation nodes is assessed based on stable intervals and behavior baselines defined according to their normal operating status. The anonymity of the network is evaluated using an improved normalized information entropy method that refines anonymity evaluation to the anonymity of each node and expands the dimensionality of evaluation features. This paper compares the effectiveness of our proposed method with static framework information entropy and single indicator methods by evaluating the degree of anonymity provided by a self-built Tor anonymous network under multiple operating scenarios including normal and under attack. Our approach utilizes dynamically changing network anonymity based on multiple anonymous attributes and better reflects the degree of anonymity in anonymous systems.

Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning

This article proposes a sensor data anonymization model that is trained on decentralized data and strikes a desirable trade-off between data utility and privacy, even in heterogeneous settings where the sensor data have different underlying distributions. Our anonymization model, dubbed Blinder, is based on a variational autoencoder and one or multiple discriminator networks trained in an adversarial fashion. We use the model-agnostic meta-learning framework to adapt the anonymization model trained via federated learning to each user’s data distribution. We evaluate Blinder under different settings and show that it provides end-to-end privacy protection on two IMU datasets at the cost of increasing privacy loss by up to 4.00% and decreasing data utility by up to 4.24%, compared to the state-of-the-art anonymization model trained on centralized data. We also showcase Blinder’s ability to anonymize the radio frequency sensing modality. Our experiments confirm that Blinder can obscure multiple private attributes at once, and has sufficiently low power consumption and computational overhead for it to be deployed on edge devices and smartphones to perform real-time anonymization of sensor data.

Application of Computer Simulation to the Anonymization of Personal Data: Synthesis-Based Anonymization Model and Algorithm

Application of Computer Simulation to the Anonymization of Personal Data: Synthesis-Based Anonymization Model and Algorithm

Secure and Energy-Efficient Network Topology Obfuscation for Software-Defined WSNs

Network topology obfuscation (NTO) is generally considered as a promising proactive mechanism to mitigate traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined wireless sensor networks (WSNs) are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. In this article, a new energy-aware NTO mechanism is proposed, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: 1) route overlapping; 2) energy consumption; 3) link costs; and 4) node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the <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> -anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods can strongly mitigate traffic analysis attacks and achieve effective NTO for software-defined WSNs. In addition, the proposed methods can reduce the success rate of the attacks while achieving lower energy consumption and higher network lifetime.

A New Anonymization Model for Privacy Preserving Data Publishing: CANON

Data privacy is a challenging trade-off problem between privacy preserving and data utility. Anonymization is a fundamental approach for privacy preserving and also a hard trade-off problem. It enables to hide the identities of data subjects or record owners and requires to be developed near-optimal solutions. In this paper, a new multidimensional anonymization model (CANON) that employs vantage-point tree (VPtree) and multidimensional generalization for greedy partitioning and anonymization, respectively, is proposed and introduced successfully for the first time. The main concept of CANON is inspired from Mondrian, which is an anonymization model for privacy preserving data publishing. Experimental results have shown that CANON takes data distribution into consideration and creates equivalence classes including closer data points than Mondrian. As a result, CANON provides better data utility than Mondrian in terms of GCP metric and it is a promising anonymization model for future works.

(k, m, t)‐anonymity: Enhanced privacy for transactional data

AbstractRecent years have witnessed the wide availability of an array of transactional datasets for mining and other research activities. A primary concern related to the public sharing of transactional datasets is identifying individuals whose data is being published. Data anonymization is a commonly utilized privacy preservation method for preventing user identification. However, the existing anonymization models such as ‐anonymity, ‐uncertainty, and (h, k, p)‐coherence for privacy preservation of transactional data do not provide complete protection from the various types of possible privacy attacks. Therefore, this article proposes a novel privacy model called (k, m, t)‐anonymity to effectively prevent identity and attribute disclosure as well as skewness attack on transactional data. A genetic algorithm‐based implementation of the model is also presented. The genetic algorithm clusters transactional data based on the similarity among the transactions for effective ‐anonymization with low information loss. The clustering algorithm simultaneously aims to minimize the skewness of data distribution in the obtained clusters for preventing skewness attack on anonymized data. Experimental results have verified that the (k, m, t)‐anonymity model ensures transactional data anonymization without significant information loss. The proposed privacy model is implemented using the proposed approach on two real‐world datasets (health domain and click‐stream data) and an enormous dataset generated synthetically (health domain consisting of 5,00,000 records). The relative error is less as compared to the relative privacy and disassociation technique for all test case scenarios. Hence, the proposed anonymization model maintains the data utility.

A new utility‐aware anonymization model for privacy preserving data publishing

AbstractMost of data in various forms contain sensitive information about individuals and so publishing such data might violate privacy. Privacy preserving data publishing (PPDP) is an essential for publishing useful data while preserving privacy. Anonymization, which is a utility based privacy preserving approach, helps hiding the identities of data subjects and also provides data utility. Since data utility is effective on the accuracy of analysis model, new anonymization algorithms to improve data utility are always required. Mondrian is one of the near‐optimal anonymization models that presents high data utility and is frequently used for PPDP. However, the upper bound problem of Mondrian causes a decrease in potential data utility. This article focuses on this problem and proposes a new utility‐aware anonymization model (u‐Mondrian). Experimental results have shown that u‐Mondrian presents an acceptable solution to the upper bound problem, increases total data utility and presents higher data utility than Mondrian for different partitioning strategies and datasets.

DPVisCreator: Incorporating Pattern Constraints to Privacy-preserving Visualizations via Differential Privacy.

Data privacy is an essential issue in publishing data visualizations. However, it is challenging to represent multiple data patterns in privacy-preserving visualizations. The prior approaches target specific chart types or perform an anonymization model uniformly without considering the importance of data patterns in visualizations. In this paper, we propose a visual analytics approach that facilitates data custodians to generate multiple private charts while maintaining user-preferred patterns. To this end, we introduce pattern constraints to model users' preferences over data patterns in the dataset and incorporate them into the proposed Bayesian network-based Differential Privacy (DP) model PriVis. A prototype system, DPVisCreator, is developed to assist data custodians in implementing our approach. The effectiveness of our approach is demonstrated with quantitative evaluation of pattern utility under the different levels of privacy protection, case studies, and semi-structured expert interviews.

Limiting sensitive values in an anonymized table while reducing information loss via p‐proportion

AbstractThe ‐proportion model bounds the proportion of sensitive values of a sensitive attribute in each equivalence class of an anonymized database table in order to limit the ability of a user to link an individual or entity to a sensitive value in that table. Nonsensitive values are not subject to any such constraints, which reduces the amount of anonymization needed to meet the requirements of this model. This leads to less information loss in an anonymized table. Anonymization is performed using an extension of the Mondrian algorithm that incorporates categorical attributes. Known as the adapted Mondrian algorithm, it generalizes a value of a categorical attribute to a set. Existing algorithms, by comparison, replace one value of a predefined hierarchy by another. The ‐proportion model is compared against the ()‐anonymity model using both the progressive local recoding and (adapted) Mondrian algorithms. Experiments demonstrate the advantage of ‐proportion and Mondrian over ()‐anonymity and progressive local recoding in terms of reduced information loss, measured using the normalized certainty penalty, discernibility metric, and classification metric.

Adaptive Privacy Preservation Approach for Big Data Publishing in Cloud using k-anonymization

Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.

ADT

Data anonymization is commonly utilized for the protection of an individual's identity when his personal or sensitive data is published. A well-known anonymization model to define the privacy of transactional data is the km-anonymity model. This model ensures that an adversary who knows up to m items of an individual cannot determine which record in the dataset corresponds to the individual with a probability greater than 1/k. However, the existing techniques generally rely on the presence of similarity between items in the dataset tuples to achieve km-anonymization and are not suitable when transactional data contains tuples without many common values. The authors refer to this type of transactional data as diverse transactional data and propose an algorithm, anonymization of diverse transactional data (ADT). ADT is based on slicing and generalization to achieve km-anonymity for diverse transactional data. ADT has been experimentally evaluated on two datasets, and it has been found that ADT yields higher privacy protection and causes a lower loss in data utility as compared to existing methods.

Research on Dynamic Social Network Anonymity Technology for Protecting Community Structure

The dynamic change of vertex degree in a dynamic social network will lead to vertex identity disclosure given the deficiencies in current privacy protection methods, such as the destruction of community structure and low data processing capability of a single workstation. The dynamic social network degree sequence anonymity (DSNDSA) method to protect community structure is proposed. The method obtains the grouping and anonymous results based on a compressed binary tree constructed by a new method called a multidimensional vector. Dummy vertices are added in parallel to construct anonymous graphs. Distributed to merge dummy vertices method based on the community is designed to reduce the number of vertices added to satisfy the anonymity model. A divide and the agglomerate algorithm is expanded for community detection. The experimental results show that the proposed algorithm based on GraphX can overcome the defects of the traditional algorithm in community protection while meeting the requirement of anonymity.

An efficient clustering-based anonymization scheme for privacy-preserving data collection in IoT based healthcare services

The healthcare services industry has seen a huge transformation since the prominent rise of the Internet of Things (IoT). IoT in healthcare services includes a large number of unified and interconnected sensors, and medical devices that generate and exchange sensitive information. Thus, an enormous amount of data is transmitted through the network which raises an alarming concern for the privacy of patient information. Therefore, privacy preserving data collection (PPDC) is on-demand to ensure the privacy of patient data. Several pieces of research on PPDC have been proposed recently. However, the research literatures have fallen short in privacy requirements and are prone to various privacy attacks. In this paper, we propose a novel privacy-preserving data collection scheme for IoT based healthcare services systems. A clustering-based anonymity model is utilized to develop an efficient privacy-preserving scheme to meet privacy requirements and to prevent healthcare IoT from various privacy attacks. We formulated the threat model as client-server-to-user to ensure privacy on both ends. On the client-side, a modified clustering-based k-anonymity model with α-deassociation is used to anonymize the data generated from the IoT nodes. The base-level privacy is then ensured through a bottom-up clustering method which generates clusters of records as per the privacy requirements. On the server-side, the cluster-combination method-UPGMA is utilized to reduce communication costs and to achieve a better level of privacy. The proposed scheme is efficient in tackling privacy attacks such as attribute disclosure, identity disclosure, membership disclosure, sensitivity attacks, similarity attacks, and skewness attacks. The effectiveness and efficiency of the proposed scheme are proven through theoretical and experimental analyses.