History-Dependent Inference Control of Queries by Dynamic Policy Adaption

Biskup, Joachim

doi:10.1007/978-3-642-22348-8_10

Cited by 9 publications

(5 citation statements)

References 10 publications

(5 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inference Threat: In [7,13,6], when a user is about to infer some information, the system prevents it by either denying access or providing scrambled data. In contrast, we assume that real organizations may need to provide access to roles to a single employee even if they allow inference.…”

Section: Discussion and Related Workmentioning

confidence: 99%

A trust-and-risk aware RBAC framework

Baracaldo

Joshi

2012

Proceedings of the 17th ACM Symposium on Access Control Models and Technologies

View full text Add to dashboard Cite

Insider Attacks are one of the most dangerous threats organizations face today. An insider attack occurs when a person authorized to perform certain actions in an organization decides to abuse the trust, and harm the organization. These attacks may negatively impact the reputation of the organization, its productivity, and may produce losses in revenue and clients. Avoiding insider attacks is a daunting task. While it is necessary to provide privileges to employees so they can perform their jobs efficiently, providing too many privileges may backfire when users accidentally or intentionally abuse their privileges. Hence, finding a middle ground, where the necessary privileges are provided and malicious usage are avoided, is necessary. In this paper, we propose a framework that extends the role-based access control (RBAC) model by incorporating a risk assessment process, and the trust the system has on its users. Our framework adapts to suspicious changes in users' behavior by removing privileges when users' trust falls below a certain threshold. This threshold is computed based on a risk assessment process that includes the risk due to inference of unauthorized information. We use a Coloured-Petri net to detect inferences. We also redefine the existing role activation problem, and propose an algorithm that reduces the risk exposure. We present experimental evaluation to validate our work.

show abstract

Section: Discussion and Related Workmentioning

confidence: 99%

A trust-and-risk aware RBAC framework

Baracaldo

Joshi

2012

Proceedings of the 17th ACM Symposium on Access Control Models and Technologies

View full text Add to dashboard Cite

show abstract

“…This threat occurs when through what seems to be innocuous information, a user is capable of inferring information that should not be accessible. In existing approaches to deal with inference threat, [12], when the user is about to conclude some unauthorized information, the system prevents it by denying access or providing scrambled data. This is only adequate for some types of organizations.…”

Section: Commercial Toolsmentioning

confidence: 99%

A Taxonomic Classification of Insider Threats: Existing Techniques, Future Directions & Recommendations

Rauf

Mohsen

Wei

2023

JCSANDM

View full text Add to dashboard Cite

In the last two decades, the number of rapidly increasing cyber incidents (i.e., data theft and privacy breaches) shows that it is becoming enormously difficult for conventional defense mechanisms and architectures to neutralize modern cyber threats in a real-time situation. Disgruntled and rouge employees/agents and intrusive applications are two notorious classes of such modern threats, referred to as Insider Threats, which lead to data theft and privacy breaches. To counter such state-of-the-art threats, modern defense mechanisms require the incorporation of active threat analytics to proactively detect and mitigate any malicious intent at the employee or application level. Existing solutions to these problems intensively rely on co-relation, distance-based risk metrics, and human judgment. Especially when humans are kept in the loop for access-control policy-related decision-making against advanced persistent threats. As a consequence, the situation can escalate and lead to privacy/data breaches in case of insider threats. To confront such challenges, the security community has been striving to identify anomalous intent for advanced behavioral anomaly detection and auto-resiliency (the ability to deter an ongoing threat by policy tuning). Towards this dimension, we aim to review the literature in this domain and evaluate the effectiveness of existing approaches per our proposed criteria. According to our knowledge, this is one of the first endeavors toward developing evaluation-based standards to assess the effectiveness of relevant approaches in this domain while considering insider employees and intrusive applications simultaneously. There have been efforts in literature towards describing and understanding insider threats in general. However, none have addressed the detection and deterrence element in its entirety, hence making our contribution one of a kind. Towards the end of this article, we enlist and discuss the existing data sets. The data sets can help understand the attributes that play crucial roles in insider threat detection. In addition, they can be beneficial for testing the newly designed security solutions in this domain. We also present recommendations for establishing a baseline standard for analyzing insider-threat data sets. This baseline standard could be used in the future to design resilient architectures and provide a road map for organizations to enhance their defense capabilities against insider threats.

show abstract

“…In the literature of controlled query evaluation [Biskup 2011;Biskup and Weibert 2008;Biskup et al 2010Biskup and Tadros 2012], a secret is preserved if for any sequence of queries there are two different models that are indistinguishable in the sense that they produce the same answers to the queries; one being a model of the secret and the other a model of the negation of the secret. Our framework also respects this property of secrecy-preservation.…”

Section: Remark Regarding the Relationship Between Cqe And Our Msq Symentioning

confidence: 99%

A Conceptual Framework for Secrecy-preserving Reasoning in Knowledge Bases

Jia

Slutzki

Honavar

2014

ACM Trans. Comput. Logic

View full text Add to dashboard Cite

In many applications, Knowledge Bases (KBs) contain confidential or private information (secrets). The KB should be able to use this secret information in its reasoning process but in answering user queries care must be exercised so that secrets are not revealed to unauthorized users. We consider this problem under the Open World Assumption (OWA) in a setting with multiple querying agents M 1 , . . . , M m that can pose queries against the KB K and selectively share answers that they receive from K with one or more other querying agents. We assume that for each M i , the KB has a prespecified set of secrets S i that need to be protected from M i . Communication between querying agents is modeled by a communication graph, a directed graph with self-loops. We introduce a general framework and propose an approach to secrecy-preserving query answering based on sound and complete proof systems. The idea is to hide the truthful answer from a querying agent M i by feigning ignorance without lying (i.e., to provide the answer 'Unknown' to a query q if it needs to be protected. Under the OWA, a querying agent cannot distinguish between the case that q is being protected (for reasons of secrecy) and the case that it cannot be inferred from K. In the pre-query stage we compute a set of envelopes E 1 , . . . , E m (restricted to a finite subset of the set of formulae that are entailed by K) so that S i ⊆ E i , and a query α posed by agent M i can be answered truthfully whenever α / ∈ E i and ¬α / ∈ E i . After the pre-query stage, the envelope is updated as needed. We illustrate this approach with two simple cases: the Propositional Horn KBs and the Description Logic AL KBs. ACM Reference Format:Jia Tao, Giora Slutzki, and Vasant Honavar. 2014. A conceptual framework for secrecy-preserving reasoning in knowledge bases.

show abstract

History-Dependent Inference Control of Queries by Dynamic Policy Adaption

Cited by 9 publications

References 10 publications

A trust-and-risk aware RBAC framework

A trust-and-risk aware RBAC framework

A Taxonomic Classification of Insider Threats: Existing Techniques, Future Directions & Recommendations

A Conceptual Framework for Secrecy-preserving Reasoning in Knowledge Bases

Contact Info

Product

Resources

About