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Smart devices have accentuated the importance of geolocation information. Geolocation identification using smart devices has paved the path for incentive-based location-based services (LBS). However, a user’s full control over a smart device can allow tampering of the location proof. Witness-oriented location proof systems (LPS) have emerged to resist the generation of false proofs and mitigate collusion attacks. However, witness-oriented LPS are still susceptible to three-way collusion attacks (involving the user, location authority, and the witness). To overcome the threat of three-way collusion in existing schemes, we introduce a decentralized consensus protocol called MobChain in this paper. In this scheme the selection of a witness and location authority is achieved through a distributed consensus of nodes in an underlying P2P network that establishes a private blockchain. The persistent provenance data over the blockchain provides strong security guarantees; as a result, the forging and manipulation of location becomes impractical. MobChain provides secure location provenance architecture, relying on decentralized decision making for the selection of participants of the protocol thereby addressing the three-way collusion problem. Our prototype implementation and comparison with the state-of-the-art solutions show that MobChain is computationally efficient and highly available while improving the security of LPS.
Faheem Zafar; Abid Khan; Saif Malik; Mansoor Ahmed; Carsten Maple; Adeel Anjum. MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus. Sensors 2021, 21, 5096 .
AMA StyleFaheem Zafar, Abid Khan, Saif Malik, Mansoor Ahmed, Carsten Maple, Adeel Anjum. MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus. Sensors. 2021; 21 (15):5096.
Chicago/Turabian StyleFaheem Zafar; Abid Khan; Saif Malik; Mansoor Ahmed; Carsten Maple; Adeel Anjum. 2021. "MobChain: Three-Way Collusion Resistance in Witness-Oriented Location Proof Systems Using Distributed Consensus." Sensors 21, no. 15: 5096.
With the advent of smart health, smart cities, and smart grids, the amount of data has grown swiftly. When the collected data is published for valuable information mining, privacy turns out to be a key matter due to the presence of sensitive information. Such sensitive information comprises either a single sensitive attribute (an individual has only one sensitive attribute) or multiple sensitive attributes (an individual can have multiple sensitive attributes). Anonymization of data sets with multiple sensitive attributes presents some unique problems due to the correlation among these attributes. Artificial intelligence techniques can help the data publishers in anonymizing such data. To the best of our knowledge, no fuzzy logic-based privacy model has been proposed until now for privacy preservation of multiple sensitive attributes. In this paper, we propose a novel privacy preserving model F-Classify that uses fuzzy logic for the classification of quasi-identifier and multiple sensitive attributes. Classes are defined based on defined rules, and every tuple is assigned to its class according to attribute value. The working of the F-Classify Algorithm is also verified using HLPN. A wide range of experiments on healthcare data sets acknowledged that F-Classify surpasses its counterparts in terms of privacy and utility. Being based on artificial intelligence, it has a lower execution time than other approaches.
Hasina Attaullah; Adeel Anjum; Tehsin Kanwal; Saif Malik; Alia Asheralieva; Hassan Malik; Ahmed Zoha; Kamran Arshad; Muhammad Imran. F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. Sensors 2021, 21, 4933 .
AMA StyleHasina Attaullah, Adeel Anjum, Tehsin Kanwal, Saif Malik, Alia Asheralieva, Hassan Malik, Ahmed Zoha, Kamran Arshad, Muhammad Imran. F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes. Sensors. 2021; 21 (14):4933.
Chicago/Turabian StyleHasina Attaullah; Adeel Anjum; Tehsin Kanwal; Saif Malik; Alia Asheralieva; Hassan Malik; Ahmed Zoha; Kamran Arshad; Muhammad Imran. 2021. "F-Classify: Fuzzy Rule Based Classification Method for Privacy Preservation of Multiple Sensitive Attributes." Sensors 21, no. 14: 4933.
Purpose Similarly, Zhu et al. (2014) and Zhang et al. (2014) stated that addressing privacy concerns with the recommendation process is necessary for the healthy development of app recommendation. Recently, Xiao et al. (2020) mentioned that a lack of effective privacy policy hinders the development of personalized recommendation services. According to the reported work, privacy protection technology methods are too limited for mobile focusing on data encryption, anonymity, disturbance, elimination of redundant data to protect the recommendation process from privacy breaches. So, this situation motivated us to conduct a systematic literature review (SLR) to provide the viewpoint of privacy and security concerns as mentioned in current state-of-the-art in the mobile app recommendation domain. Design/methodology/approach In this work, the authors have followed Kitchenham guidelines (Kitchenham and Charters, 2007) to devise the SLR process. According to the guidelines, the SLR process has three main phases: (1) define, (2) conduct the search and (3) report the results. Furthermore, the authors used systematic mapping approach as well to ensure the whole process. Findings Based on the selected studies, the authors proposed three main thematic taxonomies, including architectural style, security and privacy strategies, and user-usage in the mobile app recommendation domain. From the studies' synthesis viewpoint, it is observed that the majority of the research efforts have focused on the movie recommendation field, while the mainly used privacy scheme is homomorphic encryption. Finally, the authors suggested a set of future research dimensions useful for the potential researchers interested to perform the research in the mobile app recommendation domain. Originality/value This is an SLR article, based on existing published research, where the authors identified key issues and future directions.
Saira Beg; Saif Ur Rehman Khan; Adeel Anjum. Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review. Library Hi Tech 2021, ahead-of-p, 1 .
AMA StyleSaira Beg, Saif Ur Rehman Khan, Adeel Anjum. Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review. Library Hi Tech. 2021; ahead-of-p (ahead-of-p):1.
Chicago/Turabian StyleSaira Beg; Saif Ur Rehman Khan; Adeel Anjum. 2021. "Data usage-based privacy and security issues in mobile app recommendation (MAR): a systematic literature review." Library Hi Tech ahead-of-p, no. ahead-of-p: 1.
Smart health-care is the innovation that leads to enhanced diagnostic tools, improved patient treatment, and gadgets that ease the quality of life for majority of people. Textual clinical documents about an individual contain sensitive and semantically corelated terms. Most privacy-preserving approaches are not designed to prevent confidentiality threats. Although, recent approaches improved the utility of published output with generalized terms retrieved from several medical and general-purpose knowledge bases like SNOMED-CT and MASH. However, these models work on predefined sensitive terms using Wikipedia articles instead of authentic benchmarks. These Information Content-based methods are not capable to achieve the best balance between privacy and utility. The existing approaches guarantee syntactic privacy by sanitization but lack semantic privacy for textual clinical data. Therefore, it is imperative to design a confidentiality-aware framework to overcome these problems. Our proposed Confidentiality aware Textual Clinical Data Framework use preprocessed combinations of the terms instead of all combinations and perform automatic detection and sanitization of the sensitive and semantically correlated terms. The probabilistic sampling-based method guarantees the semantic privacy. We use high-level Petri nets to perform formal modeling of our proposed approach. Furthermore, we have also performed a detailed complexity analysis of the proposed framework.
Tehsin Kanwal; Syed A. Moqurrab; Adeel Anjum; Abid Khan; Joel J. P. C. Rodrigues; Gwanggil Jeon. Formal verification and complexity analysis of confidentiality aware textual clinical documents framework. International Journal of Intelligent Systems 2021, 1 .
AMA StyleTehsin Kanwal, Syed A. Moqurrab, Adeel Anjum, Abid Khan, Joel J. P. C. Rodrigues, Gwanggil Jeon. Formal verification and complexity analysis of confidentiality aware textual clinical documents framework. International Journal of Intelligent Systems. 2021; ():1.
Chicago/Turabian StyleTehsin Kanwal; Syed A. Moqurrab; Adeel Anjum; Abid Khan; Joel J. P. C. Rodrigues; Gwanggil Jeon. 2021. "Formal verification and complexity analysis of confidentiality aware textual clinical documents framework." International Journal of Intelligent Systems , no. : 1.
Generation of humongous data in recent times is due to the ever-growing sources, primarily the Internet of Things, which is then collected to develop effective Artificial Intelligence (AI) and machine learning (ML) solutions. Though such data provides useful insight on various trends that eventually results in better quality of life, collecting such data raises privacy concerns for data owners. Preserving the privacy of individuals during the process of data collection is an important problem specifically in the context of 1:M datasets (an individual can have multiple records). Therefore, a novel privacy-preserving data collection protocol, for 1: M datasets has been proposed in this paper. The privacy-preserving mechanism ensures data safety from external and internal privacy breaches and its effective usage in micro data analysis through AI and ML methods. The use of the leader election algorithm and the notion of l-anatomy minimize the risk of privacy disclosures and enabled us to achieve higher computational efficiency.
M. Abrar; B. Zuhaira; A. Anjum. Privacy-preserving data collection for 1: M dataset. Multimedia Tools and Applications 2021, 1 -22.
AMA StyleM. Abrar, B. Zuhaira, A. Anjum. Privacy-preserving data collection for 1: M dataset. Multimedia Tools and Applications. 2021; ():1-22.
Chicago/Turabian StyleM. Abrar; B. Zuhaira; A. Anjum. 2021. "Privacy-preserving data collection for 1: M dataset." Multimedia Tools and Applications , no. : 1-22.
The information security domain focuses on security needs at all levels in a computing environment in either the Internet of Things, Cloud Computing, Cloud of Things, or any other implementation. Data, devices, services, or applications and communication are required to be protected and provided by information security shields at all levels and in all working states. Remote authentication is required to perform different administrative operations in an information system, and Administrators have full access to the system and may pose insider threats. Superusers and administrators are the most trusted persons in an organisation. “Trust but verify” is an approach to have an eye on the superusers and administrators. Distributed ledger technology (Blockchain-based data storage) is an immutable data storage scheme and provides a built-in facility to share statistics among peers. Distributed ledgers are proposed to provide visible security and non-repudiation, which securely records administrators’ authentications requests. The presence of security, privacy, and accountability measures establish trust among its stakeholders. Securing information in an electronic data processing system is challenging, i.e., providing services and access control for the resources to only legitimate users. Authentication plays a vital role in systems’ security; therefore, authentication and identity management are the key subjects to provide information security services. The leading cause of information security breaches is the failure of identity management/authentication systems and insider threats. In this regard, visible security measures have more deterrence than other schemes. In this paper, an authentication scheme, “VisTAS,” has been introduced, which provides visible security and trusted authentication services to the tenants and keeps the records in the blockchain.
Ahmad Ali; Mansoor Ahmed; Abid Khan; Adeel Anjum; Muhammad Ilyas; Markus Helfert. VisTAS: blockchain-based visible and trusted remote authentication system. PeerJ Computer Science 2021, 7, e516 .
AMA StyleAhmad Ali, Mansoor Ahmed, Abid Khan, Adeel Anjum, Muhammad Ilyas, Markus Helfert. VisTAS: blockchain-based visible and trusted remote authentication system. PeerJ Computer Science. 2021; 7 ():e516.
Chicago/Turabian StyleAhmad Ali; Mansoor Ahmed; Abid Khan; Adeel Anjum; Muhammad Ilyas; Markus Helfert. 2021. "VisTAS: blockchain-based visible and trusted remote authentication system." PeerJ Computer Science 7, no. : e516.
The way services offered by cloud computing gets its unprecedented and undisputed popularity, so its security concerns. Among them the storage as service model (SaaS) is of the forefront of these concerns. SaaS liberates individuals and enterprises from management of IT infrastructure and data centers to concentrate on their core business. Because of untrusted and out-of-premise architecture users are reluctant to outsource their personal and important data. Encryption before outsourcing addresses some of these issues but at the same time strips the data of its useful operation such as sharing and searching. Now to address this issue, the combination of keyword based searchable encryption (KSE) and attribute-based encryption (ABE) leads to an attribute-based keyword searching (ABKS). The resultant combined concept is capable of fine-grained search operation in the multi-owner/multi-user (M/M) setting. However, the underlying costly pairing operation and complex secret sharing mechanism of ABE makes it unsuitable in practical application for resource-limited devices. On top of it, in most of the existing ABKS schemes the size of the secret key and its associated pairing operation linearly expands to the number of attributes. This paper aims at presenting a novel ABKS scheme with pairing-free access verification and constant size secret key based on AND gate access structure and ciphertext-policy (CP) framework. The security of the proposed work is reduced to the standard Decisional Diffie-Hellmen (DDH) assumption, and also collision free and error tolerant. Finally, the performance evaluation and experimental results shows that the proposed scheme improved the overall efficiency and communication overhead.
Shawal Khan; Shahzad Khan; Mahdi Zareei; Faisal Alanazi; Nazri Kama; Masoom Alam; Adeel Anjum. ABKS-PBM: Attribute-Based Keyword Search With Partial Bilinear Map. IEEE Access 2021, 9, 46313 -46324.
AMA StyleShawal Khan, Shahzad Khan, Mahdi Zareei, Faisal Alanazi, Nazri Kama, Masoom Alam, Adeel Anjum. ABKS-PBM: Attribute-Based Keyword Search With Partial Bilinear Map. IEEE Access. 2021; 9 (99):46313-46324.
Chicago/Turabian StyleShawal Khan; Shahzad Khan; Mahdi Zareei; Faisal Alanazi; Nazri Kama; Masoom Alam; Adeel Anjum. 2021. "ABKS-PBM: Attribute-Based Keyword Search With Partial Bilinear Map." IEEE Access 9, no. 99: 46313-46324.
An increasing trend in healthcare organizations to outsource EHRs’ data to the cloud highlights new challenges regarding the privacy of given individuals. Healthcare organizations outsource their EHRs data in a hybrid cloud that elevates the problem of security and privacy in terms of EHRs’ access to an unlimited number of recipients in a hybrid cloud environment. In this paper, we investigated the need for a privacy-preserving access control model for the hybrid cloud. A comprehensive and exploratory analysis of privacy-preserving solutions with the help of taxonomy for cloud-based EHRs is described in this work. We have formally identified the existence of internal access control and external privacy disclosures in outsourcing system architecture for hybrid cloud. Then, we proposed a privacy-preserving XACML based access control model (PPX-AC) that supports fine-grained access control with the multipurpose utilization of EHRs alongside state-of-the-art privacy mechanism. Our proposed approach invalidates the identified security and privacy attacks. We have formally verified the proposed privacy-preserving XACML based access control model (PPX-AC) with the invalidation of identified privacy attacks using High-Level Petri Nets (HLPN). Moreover, property verification of the proposed model in SMT-lib and Z3 solver and implementation of the model proves its effectiveness in terms of privacy-aware EHRs access and multipurpose usage.
Tehsin Kanwal; Adeel Anjum; Saif U.R. Malik; Abid Khan; Muazzam A. Khan. Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud. Computer Standards & Interfaces 2021, 78, 103522 .
AMA StyleTehsin Kanwal, Adeel Anjum, Saif U.R. Malik, Abid Khan, Muazzam A. Khan. Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud. Computer Standards & Interfaces. 2021; 78 ():103522.
Chicago/Turabian StyleTehsin Kanwal; Adeel Anjum; Saif U.R. Malik; Abid Khan; Muazzam A. Khan. 2021. "Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud." Computer Standards & Interfaces 78, no. : 103522.
Privacy preserving data publishing of electronic health record (EHRs) for 1 to M datasets with multiple sensitive attributes (MSAs) is an interesting and challenging issue. There is always a trade-off between privacy and utility in data publishing. Most of the privacy-preserving models shows critical privacy disclosure issues and, hence, they are not robust in practical datasets. The k-anonymity model is a broadly used privacy model to analyze privacy disclosures, however, this model is only useful against identity disclosure. To address the limitations of k-anonymity, a group of privacy model extensions have been proposed in past years. It includes a p-sensitive k-anonymity model, a p+-sensitive k-anonymity model, and a balanced p+-sensitive k-anonymity model. However these privacy-preserving models are not sufficient to preserve the privacy of end-users in practical datasets. In this paper we have formalize the behavior of an adversary which perform identity and attribute disclosures on balanced p+-sensitive k-anonymity model with the help of adversarial scenarios. Since balanced p+-sensitive k-anonymity model is not sufficient for 1 to M with MSAs datasets privacy preservation. We propose an extended privacy model called “1: M MSA-(p, l)-diversity” for 1: M dataset with MSAs. We then perform formal modeling and verification of the proposed model using High-Level Petri Nets (HLPN) to confirm privacy attacks invalidation. Experimental results show that our proposed “1: M MSA-(p, l)-diversity model” is efficient and provide enhanced data utility of published data.
Tehsin Kanwal; Adeel Anjum; Saif U.R. Malik; Haider Sajjad; Abid Khan; Umar Manzoor; Alia Asheralieva. A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes. Computers & Security 2021, 105, 102224 .
AMA StyleTehsin Kanwal, Adeel Anjum, Saif U.R. Malik, Haider Sajjad, Abid Khan, Umar Manzoor, Alia Asheralieva. A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes. Computers & Security. 2021; 105 ():102224.
Chicago/Turabian StyleTehsin Kanwal; Adeel Anjum; Saif U.R. Malik; Haider Sajjad; Abid Khan; Umar Manzoor; Alia Asheralieva. 2021. "A robust privacy preserving approach for electronic health records using multiple dataset with multiple sensitive attributes." Computers & Security 105, no. : 102224.
In the current hyper-connected, data-driven era, smart devices are providing access to geolocation information, enabling a paradigm shift in diverse domains. Location proof systems utilize smart devices to provide witnessed proof of location to enable secure location-based services (LBS). Applications of location proof systems include safety, asset management and operations monitoring in health care, supply chain tracking, and Internet-of-Things (IoT)-based location intelligence in businesses. In this paper, we investigate the state of the art in location proof systems, examining design challenges and implementation considerations for application in the real world. To frame the analysis, we have developed a taxonomy of location proof systems and performed a comparative analysis over the common attributes, highlighting their strength and weaknesses. Furthermore, we have identified future trends for this increasingly important area of investigation and development.
Faheem Zafar; Abid Khan; Adeel Anjum; Carsten Maple; Munam Ali Shah. Location Proof Systems for Smart Internet of Things: Requirements, Taxonomy, and Comparative Analysis. Electronics 2020, 9, 1776 .
AMA StyleFaheem Zafar, Abid Khan, Adeel Anjum, Carsten Maple, Munam Ali Shah. Location Proof Systems for Smart Internet of Things: Requirements, Taxonomy, and Comparative Analysis. Electronics. 2020; 9 (11):1776.
Chicago/Turabian StyleFaheem Zafar; Abid Khan; Adeel Anjum; Carsten Maple; Munam Ali Shah. 2020. "Location Proof Systems for Smart Internet of Things: Requirements, Taxonomy, and Comparative Analysis." Electronics 9, no. 11: 1776.
The introduction and rapid growth of the Internet of Medical Things (IoMT), a subset of the Internet of Things (IoT) in the medical and healthcare systems, has brought numerous changes and challenges to current medical and healthcare systems. Healthcare organizations share data about patients with research organizations for various medical discoveries. Releasing such information is a tedious task since it puts the privacy of patients at risk with the understanding that textual health documents about an individual contains specific sensitive terms that need to be sanitized before such document can be released. Recent approaches improved the utility of protected output by substituting sensitive terms with appropriate “generalizations” that are retrieved from several medical and general-purpose knowledge bases (KBs). However, these approaches perform unnecessary sanitization by anonymizing the negated assertions, e.g., AIDS-negative. This paper proposes a semantic privacy framework that effectively sanitizes the sensitive and semantically related terms in healthcare documents. The proposed model effectively identifies the negated assertions (e.g., AIDS-negative) before the sanitization process in IoMT which further improves the utility of sanitized documents. Moreover, besides considering the sensitive medical findings, we also incorporated state-of-the-art metrics, i.e., Protected Health Information (PHI), as defined in the privacy rules such as Health Insurance Portability and Accountability Act (HIPAA), Informatics for Integrating Biology & the Bedside (i2b2), and Materialize Interactive Medical Image Control System (MIMICS). The proposed approach is evaluated on real clinical data provided by i2b2. On average the detection (for both PHI’s and medical findings) accuracy is improved with Precision, Recall and F-measure score at 21%, 51%, and 54% respectively. The overall improved data utility of our proposed model is 8% as compared to C-sanitized and 25% when comparing it with a simple reduction approach. Experimental results show that our approach effectively manages the privacy and utility trade-off as compared to its counterparts.
Celestine Iwendi; Syed Atif Moqurrab; Adeel Anjum; Sangeen Khan; Senthilkumar Mohan; Gautam Srivastava. N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets. Computer Communications 2020, 161, 160 -171.
AMA StyleCelestine Iwendi, Syed Atif Moqurrab, Adeel Anjum, Sangeen Khan, Senthilkumar Mohan, Gautam Srivastava. N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets. Computer Communications. 2020; 161 ():160-171.
Chicago/Turabian StyleCelestine Iwendi; Syed Atif Moqurrab; Adeel Anjum; Sangeen Khan; Senthilkumar Mohan; Gautam Srivastava. 2020. "N-Sanitization: A semantic privacy-preserving framework for unstructured medical datasets." Computer Communications 161, no. : 160-171.
The Internet of Things (IoT) is an exponentially growing emerging technology, which is implemented in the digitization of Electronic Health Records (EHR). The application of IoT is used to collect the patient’s data and the data holders and then to publish these data. However, the data collected through the IoT-based devices are vulnerable to information leakage and are a potential privacy threat. Therefore, there is a need to implement privacy protection methods to prevent individual record identification in EHR. Significant research contributions exist e.g., p+-sensitive k-anonymity and balanced p+-sensitive k-anonymity for implementing privacy protection in EHR. However, these models have certain privacy vulnerabilities, which are identified in this paper with two new types of attack: the sensitive variance attack and categorical similarity attack. A mitigation solution, the θ -sensitive k-anonymity privacy model, is proposed to prevent the mentioned attacks. The proposed model works effectively for all k-anonymous size groups and can prevent sensitive variance, categorical similarity, and homogeneity attacks by creating more diverse k-anonymous groups. Furthermore, we formally modeled and analyzed the base and the proposed privacy models to show the invalidation of the base and applicability of the proposed work. Experiments show that our proposed model outperforms the others in terms of privacy security (14.64%).
Razaullah Khan; Xiaofeng Tao; Adeel Anjum; Tehsin Kanwal; Saif Malik; Abid Khan; Waheed Rehman; Carsten Maple. θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records. Electronics 2020, 9, 716 .
AMA StyleRazaullah Khan, Xiaofeng Tao, Adeel Anjum, Tehsin Kanwal, Saif Malik, Abid Khan, Waheed Rehman, Carsten Maple. θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records. Electronics. 2020; 9 (5):716.
Chicago/Turabian StyleRazaullah Khan; Xiaofeng Tao; Adeel Anjum; Tehsin Kanwal; Saif Malik; Abid Khan; Waheed Rehman; Carsten Maple. 2020. "θ-Sensitive k-Anonymity: An Anonymization Model for IoT based Electronic Health Records." Electronics 9, no. 5: 716.
Access control is used to prevent data from access of unauthorized users. Over the years, several access control models have been proposed to meet requirements of various applications and domains. Role-based access control model is one such model which enforces security based on the roles. However, role-based access control model is static in nature and does not provide the dynamism of collaboration required in the multi-domain environment. This paper presents an Ontology-Based Access Control (OBAC) model, which provides a solution by using an ontology-based approach. In OBAC model, agents are used for the identification and classification of Roles, Objects and Permissions (ROP) in distributed environment. The proposed method exploits the ontology-based approach, where agent learns and adapts changes to identify roles, objects and permissions from a given dataset and classify them into ontology according to rules and policies. The proposed ontology also provides extensibility and reusability. Moreover, we simulated our technique on datasets of two different domains. The first dataset is related to the university environment and the second one is about hospital domain. The promising experimental results indicates the effectiveness of proposed approach.
Sidra Aslam; Mansoor Ahmed; Imran Ahmed; Abid Khan; Awais Ahmad; Muhammad Imran; Adeel Anjum; Shahid Hussain. OBAC: towards agent-based identification and classification of roles, objects, permissions (ROP) in distributed environment. Multimedia Tools and Applications 2020, 79, 34363 -34384.
AMA StyleSidra Aslam, Mansoor Ahmed, Imran Ahmed, Abid Khan, Awais Ahmad, Muhammad Imran, Adeel Anjum, Shahid Hussain. OBAC: towards agent-based identification and classification of roles, objects, permissions (ROP) in distributed environment. Multimedia Tools and Applications. 2020; 79 (45-46):34363-34384.
Chicago/Turabian StyleSidra Aslam; Mansoor Ahmed; Imran Ahmed; Abid Khan; Awais Ahmad; Muhammad Imran; Adeel Anjum; Shahid Hussain. 2020. "OBAC: towards agent-based identification and classification of roles, objects, permissions (ROP) in distributed environment." Multimedia Tools and Applications 79, no. 45-46: 34363-34384.
Privacy preserving data publishing (PPDP) refers to the releasing of anonymized data for the purpose of research and analysis. A considerable amount of research work exists for the publication of data, having a single sensitive attribute. The practical scenarios in PPDP with multiple sensitive attributes (MSAs) have not yet attracted much attention of researchers. Although a recently proposed technique (p, k)-Angelization provided a novel solution, in this regard, where one-to-one correspondence between the buckets in the generalized table (GT) and the sensitive table (ST) has been used. However, we have investigated a possibility of privacy leakage through MSA correlation among linkable sensitive buckets and named it as “fingerprint correlation attack.” Mitigating that in this paper, we propose an improved solution “-anonymization” algorithm. The proposed solution thwarts the attack using some privacy measures and improves the one-to-one correspondence to one-to-many correspondence between the buckets in GT and ST which further reduces the privacy risk with increased utility in GT. We have formally modelled and analysed the attack and the proposed solution. Experiments on the real-world datasets prove the outperformance of the proposed solution as compared to its counterpart.
Razaullah Khan; Xiaofeng Tao; Adeel Anjum; Haider Sajjad; Saif Ur Rehman Malik; Abid Khan; Fatemeh Amiri. Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity. Wireless Communications and Mobile Computing 2020, 2020, 1 -18.
AMA StyleRazaullah Khan, Xiaofeng Tao, Adeel Anjum, Haider Sajjad, Saif Ur Rehman Malik, Abid Khan, Fatemeh Amiri. Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity. Wireless Communications and Mobile Computing. 2020; 2020 ():1-18.
Chicago/Turabian StyleRazaullah Khan; Xiaofeng Tao; Adeel Anjum; Haider Sajjad; Saif Ur Rehman Malik; Abid Khan; Fatemeh Amiri. 2020. "Privacy Preserving for Multiple Sensitive Attributes against Fingerprint Correlation Attack Satisfying c-Diversity." Wireless Communications and Mobile Computing 2020, no. : 1-18.
Cancer is the second leading cause of mortality across the globe. Approximately 9.6 million people are estimated to have died due to cancer disease in 2019. Accurate and early prediction of cancer can assist healthcare professionals to devise timely therapeutic innervations to control sufferings and the risk of mortality. Generally, a machine learning (ML) based predictive system in healthcare uses data (genetic profile or clinical parameters) and learning algorithms to predict target values for cancer detection. However, optimization of predictive accuracy is an important endeavor for accurate decision making. Reject Option (RO) classifiers have been used to improve the predictive accuracy of classifiers for cancer like complex problems. In a gene profile all of the features are not important and should be shaved off. ML offers different techniques with their own methodology for feature selection (FS) and the classification results are dependent on the datasets each having its own distribution and features. Therefore, both FS methods and ML algorithms with RO need to be taken into account for robust classification. The main objective of this study is to optimize three parameters (learning algorithm, FS method and rejection rate) for robust cancer prediction rather than considering two traditional parameters (learning algorithm and rejection rate). The analysis of different FS methods (including t-test, Las Vegas Filter (LVF), Relief, and Information Gain (IG)) and RO classifiers on different rejection thresholds is performed to investigate the robust predictability of cancer. The three cancer datasets (Colon cancer, Leukemia and Breast cancer) were reduced using different FS methods and each of them were used to analyze the predictability of cancer using different RO classifiers. The results reveal that for each dataset predictive accuracies of RO classifiers were different for different FS methods. The findings based on proposed scheme indicate that, the ML algorithms along with their dependence on suitable FS methods need to be taken into consideration for accurate prediction.
Muhammad Hammad Waseem; Malik Sajjad Ahmed Nadeem; Assad Abbas; Aliya Shaheen; Wajid Aziz; Adeel Anjum; Umar Manzoor; Muhammad A. Balubaid; Seong-O Shim. On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction. IEEE Access 2019, 7, 141072 -141082.
AMA StyleMuhammad Hammad Waseem, Malik Sajjad Ahmed Nadeem, Assad Abbas, Aliya Shaheen, Wajid Aziz, Adeel Anjum, Umar Manzoor, Muhammad A. Balubaid, Seong-O Shim. On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction. IEEE Access. 2019; 7 (99):141072-141082.
Chicago/Turabian StyleMuhammad Hammad Waseem; Malik Sajjad Ahmed Nadeem; Assad Abbas; Aliya Shaheen; Wajid Aziz; Adeel Anjum; Umar Manzoor; Muhammad A. Balubaid; Seong-O Shim. 2019. "On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction." IEEE Access 7, no. 99: 141072-141082.
Past and ongoing decades have witnessed significant uplift in data generation due to ever growing sources of data. Collection and aggradation of such huge data have triggered serious concerns on privacy of data-owners’ sensitive information. Catering this, several existing anonymization models proffer privacy-preserving data collection. However, the models put-forth either strict or unrealistic assumptions regarding leaders’ selection (the concept of first and last leaders in data collection process). In this paper, we have identified and formally defined a privacy attack, Leader Collusion Attack (LCA); where first and second leaders may collude to breech individuals’ privacy during data collection process. In this regard, we have proposed a novel k-anonymity based dynamic data collection protocol (presented single leader election) to mitigate LCA. Moreover, we have formally modelled and analysed the proposed protocol through HLPNs and demonstrated the mitigation of LCA. Experimentations on real-world datasets advocate the outperformance of our protocol over existing model in terms of better utility and privacy levels.
Haider Sajjad; Tehsin Kanwal; Adeel Anjum; Saif Ur Rehman Malik; Ahmed Khan; Abid Khan; Umar Manzoor. An efficient privacy preserving protocol for dynamic continuous data collection. Computers & Security 2019, 86, 358 -371.
AMA StyleHaider Sajjad, Tehsin Kanwal, Adeel Anjum, Saif Ur Rehman Malik, Ahmed Khan, Abid Khan, Umar Manzoor. An efficient privacy preserving protocol for dynamic continuous data collection. Computers & Security. 2019; 86 ():358-371.
Chicago/Turabian StyleHaider Sajjad; Tehsin Kanwal; Adeel Anjum; Saif Ur Rehman Malik; Ahmed Khan; Abid Khan; Umar Manzoor. 2019. "An efficient privacy preserving protocol for dynamic continuous data collection." Computers & Security 86, no. : 358-371.
State-of-the-art progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. A hybrid cloud is an infrastructure consisting of a private cloud (managed by the organization) and a public cloud (managed by the cloud service provider). The use of hybrid cloud enables electronic health records to be exchanged between medical institutions and supports multipurpose usage of electronic health records. Along with the benefits, cloud-based electronic health records also raise the problems of security and privacy specifically in terms of electronic health records access. A comprehensive and exploratory analysis of privacy-preserving solutions revealed that most current systems do not support fine-grained access control or consider additional factors such as privacy preservation and relationship semantics. In this article, we investigated the need of a privacy-aware fine-grained access control model for the hybrid cloud. We propose a privacy-aware relationship semantics–based XACML access control model that performs hybrid relationship and attribute-based access control using extensible access control markup language. The proposed approach supports fine-grained relation-based access control with state-of-the-art privacy mechanism named Anatomy for enhanced multipurpose electronic health records usage. The proposed (privacy-aware relationship semantics–based XACML access control model) model provides and maintains an efficient privacy versus utility trade-off. We formally verify the proposed model (privacy-aware relationship semantics–based XACML access control model) and implemented to check its effectiveness in terms of privacy-aware electronic health records access and multipurpose utilization. Experimental results show that in the proposed (privacy-aware relationship semantics–based XACML access control model) model, access policies based on relationships and electronic health records anonymization can perform well in terms of access policy response time and space storage.
Tehsin Kanwal; Ather Abdul Jabbar; Adeel Anjum; Saif Ur Malik; Abid Khan; Naveed Ahmad; Umar Manzoor; Muhammad Naeem Shahzad; Muhammad A Balubaid. Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud. International Journal of Distributed Sensor Networks 2019, 15, 1 .
AMA StyleTehsin Kanwal, Ather Abdul Jabbar, Adeel Anjum, Saif Ur Malik, Abid Khan, Naveed Ahmad, Umar Manzoor, Muhammad Naeem Shahzad, Muhammad A Balubaid. Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud. International Journal of Distributed Sensor Networks. 2019; 15 (6):1.
Chicago/Turabian StyleTehsin Kanwal; Ather Abdul Jabbar; Adeel Anjum; Saif Ur Malik; Abid Khan; Naveed Ahmad; Umar Manzoor; Muhammad Naeem Shahzad; Muhammad A Balubaid. 2019. "Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud." International Journal of Distributed Sensor Networks 15, no. 6: 1.
Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-processor scheduling technique for scheduling. The simulation results show that the proposed approach reduces up to 5% of the temperature peaks and variations in a uni-processor environment while reduces up to 7% and 6% of the temperature spatial gradient and the average temperature in multi-processor environment, respectively.
Muhammad Naeem Shehzad; Qaisar Bashir; Ghufran Ahmad; Adeel Anjum; Muhammad Naeem Awais; Umar Manzoor; Zeeshan Azmat Shaikh; Muhammad A Balubaid; Tanzila Saba. Thermal-aware resource allocation in earliest deadline first using fluid scheduling. International Journal of Distributed Sensor Networks 2019, 15, 1 .
AMA StyleMuhammad Naeem Shehzad, Qaisar Bashir, Ghufran Ahmad, Adeel Anjum, Muhammad Naeem Awais, Umar Manzoor, Zeeshan Azmat Shaikh, Muhammad A Balubaid, Tanzila Saba. Thermal-aware resource allocation in earliest deadline first using fluid scheduling. International Journal of Distributed Sensor Networks. 2019; 15 (3):1.
Chicago/Turabian StyleMuhammad Naeem Shehzad; Qaisar Bashir; Ghufran Ahmad; Adeel Anjum; Muhammad Naeem Awais; Umar Manzoor; Zeeshan Azmat Shaikh; Muhammad A Balubaid; Tanzila Saba. 2019. "Thermal-aware resource allocation in earliest deadline first using fluid scheduling." International Journal of Distributed Sensor Networks 15, no. 3: 1.
Preserved privacy and enhanced utility are two competing requirements in data publishing. For maintaining a trade-off between the two; a plethora of research work exist in 1:1 scenario (each individual has a single record) with a single sensitive attribute (SA). However, some practical scenarios i.e., data having 1:M records (an individual can have multiple records) with multiple sensitive attributes (MSAs), have been relatively understudied. In our current interconnected and digitalized society, the capability to deal with such scenarios is increasingly important due to the ever-increasing sources of data that could be drawn together and infer one's information (e.g. profile and lifestyle) and consequently, compromising one's privacy. In this paper, we present a new type of attack on 1:M records with MSAs, coined as MSAs generalization correlation attacks and perform formal modeling and analysis of these attacks. Then, we propose a privacy-preserving technique “(p, l)-Angelization” for 1:M–MSAs data publication. Extensive experiments over real-world datasets advocate the outperformance of our technique over its counterparts.
Tehsin Kanwal; Sayed Ali Asjad Shaukat; Adeel Anjum; Saif Ur Rehman Malik; Kim-Kwang Raymond Choo; Abid Khan; Naveed Ahmad; Mansoor Ahmad; Samee U. Khan. Privacy-preserving model and generalization correlation attacks for 1:M data with multiple sensitive attributes. Information Sciences 2019, 488, 238 -256.
AMA StyleTehsin Kanwal, Sayed Ali Asjad Shaukat, Adeel Anjum, Saif Ur Rehman Malik, Kim-Kwang Raymond Choo, Abid Khan, Naveed Ahmad, Mansoor Ahmad, Samee U. Khan. Privacy-preserving model and generalization correlation attacks for 1:M data with multiple sensitive attributes. Information Sciences. 2019; 488 ():238-256.
Chicago/Turabian StyleTehsin Kanwal; Sayed Ali Asjad Shaukat; Adeel Anjum; Saif Ur Rehman Malik; Kim-Kwang Raymond Choo; Abid Khan; Naveed Ahmad; Mansoor Ahmad; Samee U. Khan. 2019. "Privacy-preserving model and generalization correlation attacks for 1:M data with multiple sensitive attributes." Information Sciences 488, no. : 238-256.
Zineddine Kouahla; Adeel Anjum; Hamid Seridi. Indexing through separable partitioning for complex data sharing in P2P systems. Journal of Intelligent & Fuzzy Systems 2018, 35, 6469 -6478.
AMA StyleZineddine Kouahla, Adeel Anjum, Hamid Seridi. Indexing through separable partitioning for complex data sharing in P2P systems. Journal of Intelligent & Fuzzy Systems. 2018; 35 (6):6469-6478.
Chicago/Turabian StyleZineddine Kouahla; Adeel Anjum; Hamid Seridi. 2018. "Indexing through separable partitioning for complex data sharing in P2P systems." Journal of Intelligent & Fuzzy Systems 35, no. 6: 6469-6478.