Meet Privacy Requirements Research Articles

Design of privacy preserving model based on clustering involved anonymization along with feature selection

Healthcare services has become a hug transformation due to the number of disease emerging at presently. Accordingly, enormous amount of data is generated regarding on the patient data. Hence, preserving privacy of patient data is the major concern to meet privacy requirements and to prevent healthcare data from numerous privacy attacks. To protect the information from privacy attack, various studies were performed to safeguard the patient's data but, these methods have several issues when preserving privacy that increases proportionally as the data dimension increased in multiple approaches, which produces less data quality, more information loss and more execution time.Thus, selecting relevant attributes plays an important factor for improving the efficiency of any preserving algorithm. In order to overcome these concerns, in this paper proposed a privacy preserving model based on clustering involved anonymization along with feature selection approaches. The proposed model consist of two phases such as, feature selection and anonymization. In the initial phase, the relevant features are selected using Symmetrical Uncertainty (SU) and the redundant features present in the dataset are removed utilizing Spearman's Correlation Coefficient. In the second phase, the privacy preservation is performed using Utility Preserved Anonymization (UPA) algorithm. Also, the proposed algorithm reduces the dimensionality of data to ease the process of forming clusters for anonymization. The experimental analysis using real time datasets to verifies the effectiveness of the proposed method. The results show high sensitivity (up to 98.63%) and high accuracy (up to 98%), allowing us to claim efficient attribute selection for anonymization. Hence proved the proposed method reduces clustering's complexity by removing the irrelevant attributes effectively.

Blossom: Cluster-Based Routing for Preserving Privacy in Opportunistic Networks

Opportunistic networks are an enabler technology for typologies without centralized infrastructure. Portable devices, such as wearable and embedded mobile systems, send relay messages to the communication range devices. One of the most critical challenges is to find the optimal route in these networks while at the same time preserving privacy for the participants of the network. Addressing this challenge, we presented a novel routing algorithm based on device clusters, reducing the overall message load and increasing network performance. At the same time, possibly identifying information of network nodes is eliminated by cloaking to meet privacy requirements. We evaluated our routing algorithm in terms of efficiency and privacy in opportunistic networks of traditional and structured cities, i.e., Venice and San Francisco by comparing our approach against the PRoPHET, First Contact, and Epidemic routing algorithms. In the San Francisco and Venice scenarios, Blossom improves messages delivery probability and outperforms PRoPHET, First Contact, and Epidemic by 46%, 100%, and 160% and by 67%, 78%, and 204%, respectively. In addition, the dropped messages probability in Blossom decreased 83% compared to PRoPHET and Epidemic in San Francisco and 91% compared to PRoPHET and Epidemic in Venice. Due to the small number of messages generated, the network overhead in this algorithm is close to zero. The network overhead can be significantly reduced by clustering while maintaining a reliable message delivery.

A PLS-HECC-based device authentication and key agreement scheme for smart home networks

IoT devices permeate our society, collect personal data, and support critical infrastructures such as the healthcare. Therefore, there is a critical need for authentication and authorization schemes for IoT devices to meet privacy requirements, such as mutual authentication and user anonymity, as well as robustness against security attacks. In this paper, we propose a device authentication and key agreement scheme for IoT networks. Our proposal takes as a model the scheme proposed by Rezai et al., and combines it with a physical layer security technique and a hyper-elliptic curve cryptosystem . Our results show that not only our authentication scheme provides anonymity, mutual authentication, and efficiency, but it also provides resilience to various attacks, including man-in-the-middle, replay, and de-synchronization attacks. Our comparison shows that our scheme performs better than the state-of-the-art in terms of security properties, while adding a small overhead of ≈ 10 ( ms ) .

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.

BPTM: Blockchain-Based Privacy-Preserving Task Matching in Crowdsourcing

Task matching in crowdsourcing is designed to provide convenient task information retrieval and has been extensively explored. In general, the task matching process is required to be reliable and to meet privacy requirements. However, most existing privacy-preserving task matching solutions for crowdsourcing focus on privacy issues but ignore the reliability of the process. In this paper, we propose a blockchain-based task matching scheme for crowdsourcing with a secure and reliable matching. Instead of utilizing a centralized cloud server, we employ smart contracts, an emerging blockchain technology, to provide reliable and transparent matching. In this way, data confidentiality and identity anonymity are achieved effectively and efficiently. The extensive privacy analysis and performance evaluation show that our solution is secure and feasible.

Sleeping posture recognition using fuzzy c-means algorithm

BackgroundPressure sensors have been used for sleeping posture detection, which meet privacy requirements. Most of the existing techniques for sleeping posture recognition used force-sensitive resistor (FSR) sensors. However, lower limbs cannot be recognized accurately unless thousands of sensors are deployed on the bedsheet.MethodWe designed a sleeping posture recognition scheme in which FSR sensors were deployed on the upper part of the bedsheet to record the pressure distribution of the upper body. In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures.ResultsThe experimental results showed that the proposed method achieved an accuracy of above 88%. Moreover, the proposed scheme is cost-efficient and easy to deploy.ConclusionsThe proposed sleeping posture recognition system can be used for pressure ulcer prevention and sleep quality assessment. Compared to wearable sensors and cameras, FSR sensors and infrared array sensors are unobstructed and meet privacy requirements. Moreover, the proposed method provides a cost-effective solution for the recognition of sleeping posture.

A Framework to Secure Medical Image Storage in Cloud Computing Environment

Nowadays, modern healthcare providers create massive medical images every day because of the recent progress in imaging tools. This is generally due to the increasing number of patients demanding medical services. This has resulted in a continuous demand of a large storage space. Unfortunately, healthcare domains still use local data centers for storing medical data and managing business processes. This has significant negative impacts on operating costs associated with licensing fees and maintenance. To overcome these challenges, healthcare organizations are interested in adopting cloud storage rather than on-premise hosted solutions. This is mainly justified by the scalability, cost savings and availability of cloud services. The primary objective of this model is to outsource data and delegate IT computations to an external party. The latter delivers needed storage systems via the Internet to fulfill client's demands. Even though this model provides significant cost advantages, using cloud storage raises security challenges. To this aim, this article describes several solutions which were proposed to ensure data protection. The existing implementations suffer from many limitations. The authors propose a framework to secure the storage of medical images over cloud computing. In this regard, they use multi-region segmentation and watermarking techniques to maintain both confidentiality and integrity. In addition, they rely on an ABAC model to ensure access control to cloud storage. This solution mainly includes four functions, i.e., (1) split data for privacy protection, (2) authentication for medical dataset accessing, (3) integrity checking, and (4) access control to enforce security measures. Hence, the proposal is an appropriate solution to meet privacy requirements.

Examining the intended and unintended consequences of organisational privacy safeguards

Research shows that despite organisational efforts to achieve privacy compliance, privacy breaches continue to rise. The extant studies on organisational privacy compliance concentrate on the extent to which privacy threats can be alleviated through a combination of technical and human controls and the positive (and often intended) influences of these controls. This focus inadvertently neglects unintended consequences such as impeded workflow in medical practices. To address this research conflict, this study uses an interpretive grounded theory research approach to investigate the consequences of privacy safeguard enactment in medical practices, including whether it influences their ability to meet privacy requirements and whether workflows are impeded. Our central contribution is a theoretical framework, the unintended consequences of privacy safeguard enactment (UCPSE) framework, which explicates the process by which privacy safeguards are evaluated and subsequently bypassed and the resulting influence on organisational compliance. The UCPSE highlights the importance of the imbalance challenge, which is the result of unintended consequences outweighing the intended consequences of privacy safeguard enactment. Failure to address the imbalance challenge leads to the adoption of workarounds that may ultimately harm the organisation’s privacy compliance. Despite several research calls, the consequences and effectiveness of organisational privacy efforts are largely missing from both information systems and health informatics research. This study is one of the first attempts to both systematically identify the impacts of privacy safeguard enactment and to examine its implications for privacy compliance in the healthcare domain. The findings also have practical implications for healthcare executives on the UCPSE and how they could alleviate the imbalance challenge to thwart workarounds and the subsequent negative effects on privacy compliance.

A Novel Location Privacy Preservation Method for Moving Object

Location privacy has been a serious concern for mobile users who use location-based services to acquire geographical location. Spatial cloaking technique is a well-known privacy preserving method, which blurs an exact user location into a cloaked region to meet privacy requirements. In this paper, we propose a new semantic privacy preservation method rely on the well-established k-anonymity and l-diversity privacy metrics for semantic cloaking. We also define a representative cloaking region which helps in communication cost reduction caused by user movement. Experimental implementation and analysis exhibit that our proposed method renders good performance in efficiency and scalability. We also show that our proposed method outperforms the existing privacy preservation method by effectively enhance privacy against various adversaries.

A spatial transformation scheme supporting data privacy and query integrity for security of outsourced databases

ABSTRACTOutsourcing database to a third‐party data provider is becoming a common practice for data owners to avoid the cost of managing and maintaining the database. Meanwhile, because of the popularity of location‐based services, the need for spatial data is increasing dramatically. However, the most important challenge in database outsourcing is how to meet privacy requirements and guarantee the integrity of the query result as well. Unfortunately, most of the existing techniques support either data privacy or integrity on spatial databases. To carry on both privacy and integrity for outsourced spatial data, we propose a spatial transformation scheme that makes use of shearing transformation with rotation shifting. We describe attack models measuring the data privacy of our transformation scheme. Finally, our extensive experiments have demonstrated that our scheme has adequate strength for data privacy by showing outstanding performance against different kinds of attack models and efficiently handles the query integrity of the query result sets. Copyright © 2013 John Wiley & Sons, Ltd.

이동 객체 정보 보호를 위한 그리드 기반 시멘틱 클로킹 기법

최근 스마트폰의 발전에 따라서 많은 위치 기반 서비스가 활용되고 있으며, 위치 정보 노출로 인한 문제점이 사회적 이슈로 대두되고 있다. 기존의 잘 알려진 위치 정보 보호를 위한 공간 클로킹 기법은 사용자가 요청한 지역에서 위치 정보를 흐릿하게 처리하였다. 하지만 계속적으로 움직이는 이동 객체의 모든 지역을 클로킹하기에는 범위공간이 무수히 넓어지는 문제를 가진다. 따라서, 본 논문에서는 이동 객체 정보 보호를 위한 그리드 기반 시멘틱 클로킹 기법을 제안한다. 제안 기법은 시멘틱 클로킹을 위하여 EMD 갱신 스키마를 확장하고 이동 객체를 위한 대표 보호지역의 클로킹을 정의하였다. 성능 평가에서는 제안 기법이 기존 기법에 비해 처리 시간과 공간 범위에서 안전성과 효율성을 높였다. 이를 통해, 성공적으로 다양한 적으로부터 지속적으로 움직이는 객체의 위치 개인 정보를 보호하여 기존의 방법을 능가하는 성능을 보인다. Location privacy has been a serious concern for mobile users who use location-based services to acquire geographical location continuously. Spatial cloaking technique is a well-known privacy preserving method, which blurs an exact user location into a cloaked area to meet privacy requirements. However, cloaking for continuous moving object suffers from cloaked area size problem as it is unlikely for all objects travel in the same direction. In this paper, we propose a grid-based privacy preservation method with an improved Earth Mover's Distance(EMD) metric weight update scheme for semantic cloaking. We also define a representative cloaking area which protects continuous location privacy for moving users. Experimental implementation and evaluation exhibit that our proposed method renders good efficiency and scalability in cloaking processing time and area size control. We also show that our proposed method outperforms the existing method by successfully protects location privacy of continuous moving objects against various adversaries.

Privacy preservation in data mining using hybrid perturbation methods: an application to bankruptcy prediction in banks

Today, the data related to business, finance and healthcare pose problems for Privacy-Preserving Data Mining (PPDM). Privacy regulations and concerns prevent data owners from sharing data for mining purposes. To circumvent this problem, data owners must design strategies to meet privacy requirements and ensure valid data mining results. This paper proposes the hybridisation of the random projection and random rotation methods for privacy-preserving classification. The hybrid method is tested on six benchmark data sets and four bank bankruptcy data sets. These methods ensure the privacy and secrecy of bank data and the resulting data set is mined without a considerable loss of accuracy. A multilayer perceptron, decision tree J48 and logistic regression are used as classifiers. The results of a tenfold cross-validation and t-test indicate improved average accuracies for the hybrid privacy preservation method compared to when random projection is used alone. The reasons for the superior performance of the hybrid privacy preservation method are also highlighted.

The SPARTA pseudonym and authorization system

This paper deals with privacy-preserving (pseudonymized) access to a service resource. In such a scenario, two opposite needs seem to emerge. On one side, the service provider may want to control, in first place, the user accessing its resources, i.e., without being forced to delegate the issuing of access permissions to third parties to meet privacy requirements. On the other side, it should be technically possible to trace back the real identity of a user upon dishonest behavior, and of course, this must be necessary accomplished by an external authority distinct from the provider itself. The framework described in this paper aims at coping with these two opposite needs. This is accomplished through (i) a distributed third-party-based infrastructure devised to assign and manage pseudonym certificates, decoupled from (ii) a two-party procedure, devised to bind an authorization permission to a pseudonym certificate with no third-party involvement. The latter procedure is based on a novel blind signature approach which allows the provider to blindly verify, at service subscription time, that the user possesses the private key of the still undisclosed pseudonym certificate, thus avoiding transferability of the authorization permission.

SVDC: Preserving Privacy in Clustering using Singular Value Decomposition

AbstractProtecting privacy from unauthorized access is one of the primary concerns in data use, from national security to business transactions. It creates a new branch of data mining known as Privacy Preserving Data Mining (PPDM). Privacy-Preserving is a major concern in the application of data mining techniques to datasets containing personal, sensitive, or confidential information. Data distortion is a critical component to preserve privacy in security-related data mining applications; we propose a Singular Value Decomposition (SVD) method for data distortion. We focus primarily on privacy preserving data clustering. Our proposed method, Singular Value Decomposition Clustering (SVDC) distorts only confidential numerical attributes to meet privacy requirements, while preserving general features for clustering analysis.

The SPARTA Pseudonym and Authorization System

This paper deals with privacy-preserving (pseudonymized) access to a service resource. In such a scenario, two opposite needs seem to emerge. On one side, the service provider may want to control in first place the user accessing its resources, i.e., without being forced to delegate the management of access permissions to third parties to meet privacy requirements. On the other side, it should be technically possible to trace back the real identity of an user upon dishonest behavior, and of course this must be necessary accomplished by an external authority distinct from the provider itself. The framework described in this paper aims at coping with these two opposite needs. This is accomplished through i) a distributed third-party-based instrastructure devised to assign and manage pseudonym certificates, decoupled from ii) a two-party procedure, devised to bind an authorization permission to a pseudonym certificate with no third-party involvement. The latter procedure is based on a novel blind signature approach which allows the provider to blindly verify, at registration time, that the user possesses the private key of the still undisclosed pseudonym certificate, thus avoiding transferability of the authorization permission.

Privacy-Preserving Clustering to Uphold Business Collaboration

While the sharing of data is known to be beneficial in data mining applications and widely acknowledged as advantageous in business, this information sharing can become controversial and thwarted by privacy regulations and other privacy concerns. Data clustering for instance could be more accurate if more information is available, hence the data sharing. Any solution needs to balance the clustering requirements and the privacy issues. Rather than simply hindering data owners from sharing information for data analysis, a solution could be designed to meet privacy requirements and guarantee valid data clustering results. To achieve this dual goal, this article introduces a method for privacy-preserving clustering called dimensionality reduction-based transformation (DRBT). This method relies on the intuition behind random projection to protect the underlying attribute values subjected to cluster analysis. It is shown analytically and empirically that transforming a dataset using DRBT, a data owner can achieve privacy preservation and get accurate clustering with little overhead of communication cost. Such a method presents the following advantages: it is independent of distance-based clustering algorithms, it has a sound mathematical foundation, and it does not require CPU-intensive operations.