Towards Policy Retrieval for Provenance Based Access Control Model

Pei, Jisheng; Yao, Xiaojun

doi:10.1109/trustcom.2014.101

Cited by 7 publications

(4 citation statements)

References 13 publications

(18 reference statements)

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“…Used in # of studies Percentage SVM [51], [39], [56], [65], [46], [64], [40], [59], [38], [74], [8], [75], [62], [76], [77], [37] 16 25 % RF [50], [41], [44], [26], [64], [29], [59], [60], [10], [33], [34], [13], [37], [69] 14 22 % DT [9], [42], [25], [71], [72], [49], [66], [33], [77] 9 14 % KNN [41], [45] , [46], [66], [59], [10] [8], [77], [43], [70], [73], S15, [53],…”

Section: Methodsmentioning

confidence: 99%

“…% AnonyT, APRP, AuthT, completeness, coverage, ERP, EUR, fitness, generality, InACRR, incompleteness, MER, NRD/P, PCR, RAUti, RPA, scalability, stability, WAP [48], [60], [55], [49], [54], [58], [58], [61], [54], [50], [73], [68], [55], [10], [75], [51], [63], [54], [44] (respectively) 1 each metric 30 % evaluation metrics were often used within a single study, supporting the results depicted in Figure 10.…”

Section: H Performance Metrics (Rq8)mentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Ait el hadj

2023

IJCDS

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Access control (AC) systems are crucial for safeguarding sensitive data and resources, yet the increasing complexity of dynamic environments has underscored the need for more accurate and efficient solutions. Therefore, there is a growing need to enhance the accuracy, efficiency, and decision-making capabilities of AC systems. Machine learning (ML) techniques offer promising solutions to address these challenges and automate access control processes. This paper conducts a systematic review of ML techniques in AC systems, involving a comprehensive analysis covering the identification and classification of ML models and their specific applications. Through a meticulous examination of 62 relevant studies published between 2000 and 2023, the review reveals a predominant focus on innovative solutions and adaptations to enhance access control decision-making. Comparative studies play a significant role in assessing different ML approaches, with a focus on identifying the most effective methods for addressing access control challenges. Notably, Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC) emerge as the predominant models, while Support Vector Machine (SVM) tops the list as the most commonly employed ML technique, followed by Random Forest (RF) and Decision Tree (DT). The review evaluates the performance and effectiveness of ML models in AC systems, highlighting their strengths and limitations. Additionally, it addresses significant challenges in the field and identifies potential directions for future research. This systematic review provides valuable insights into the current state of ML-based access control systems, fostering further advancements in this vital domain.

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Section: Methodsmentioning

confidence: 99%

Section: H Performance Metrics (Rq8)mentioning

confidence: 99%

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Ait el hadj

2023

IJCDS

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“…It provides dynamic separation of duties, origin-based control and objects versioning in environments like WBSNs. Pei et al [25] define a framework to capture data provenance and create access control policies from collected data being possible its application in WBSNs. Cheng et al [9] look for the administrative management of a relationship-based access control model including provenance management.…”

Section: Related Workmentioning

confidence: 99%

$$SoNeUCON_{ABC}Pro$$SoNeUCONABCPro: An Access Control Model for Social Networks with Translucent User Provenance

González-Manzano

Slaymaker

Fuentes

et al. 2018

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract. Web-Based Social Networks (WBSNs) are used by millions of people worldwide. While WBSNs provide many benefits, privacy preservation is a concern. The management of access control can help to assure data is accessed by authorized users. However, it is critical to provide sufficient flexibility so that a rich set of conditions may be imposed by users. In this paper we coin the term user provenance to refer to tracing users actions to supplement the authorisation decision when users request access. For example restricting access to a particular photograph to those which have "liked" the owners profile. However, such a tracing of actions has the potential to impact the privacy of users requesting access. To mitigate this potential privacy loss the concept of translucency is applied. This paper extends SoN eU CONABC model and presents SoN eU CONABC P ro, an access control model which includes translucent user provenance. Entities and access control policies along with their enforcement procedure are formally defined. The evaluation demonstrates that the system satisfies the imposed goals and supports the feasibility of this model in different scenarios.

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“…We believe that by allowing more flexible control of approximated data provenance summarization using entropy measurements, a wide scope of provenancerelated tasks, for example, provenance based access control rules retrieval [24], provenance visualization [25], and provenance storage [26,27], can be performed both more effectively and more efficiently.…”

Section: Related Workmentioning

confidence: 99%

Information-Balance-Aware Approximated Summarization of Data Provenance

Pei

Yao

2017

Scientific Programming

Self Cite

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Extracting useful knowledge from data provenance information has been challenging because provenance information is often overwhelmingly enormous for users to understand. Recently, it has been proposed that we may summarize data provenance items by grouping semantically related provenance annotations so as to achieve concise provenance representation. Users may provide their intended use of the provenance data in terms of provisioning, and the quality of provenance summarization could be optimized for smaller size and closer distance between the provisioning results derived from the summarization and those from the original provenance. However, apart from the intended provisioning use, we notice that more dedicated and diverse user requirements can be expressed and considered in the summarization process by assigning importance weights to provenance elements. Moreover, we introduce information balance index (IBI), an entropy based measurement, to dynamically evaluate the amount of information retained by the summary to check how it suits user requirements. An alternative provenance summarization algorithm that supports manipulation of information balance is presented. Case studies and experiments show that, in summarization process, information balance can be effectively steered towards user-defined goals and requirement-driven variants of the provenance summarizations can be achieved to support a series of interesting scenarios.

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Towards Policy Retrieval for Provenance Based Access Control Model

Cited by 7 publications

References 13 publications

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

$$SoNeUCON_{ABC}Pro$$SoNeUCONABCPro: An Access Control Model for Social Networks with Translucent User Provenance

Information-Balance-Aware Approximated Summarization of Data Provenance

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