How Location-Aware Access Control Affects User Privacy and Security in Cloud Computing Systems

open access articleThe use of cloud computing (CC) is rapidly increasing due to the demand for internet services and communications. The large number of services and data stored in the cloud creates security risks due to the dynamic movement of data, connected devices and users between various cloud environments. In this study, we will develop an innovative prototype for location-aware access control and data privacy for CC systems. We will apply location-aware access control policies to role-based access control of Cloud Foundry, and then analyze the impact on user privacy after implementing these policies. This innovation can be used to address the security risks introduced by inter-cloud use and communication, and will have significant impact in making citizen’s personal data more secure

Preserving Privacy in Cyber-physical-social systems: An Anonymity and Access Control Approach

With the significant development of mobile commerce, the integration of physical, social, and cyber worlds is increasingly common. The term Cyber Physical Social Systems is used to capture technology’s human-centric role. With the revolutionization of CPSS, privacy protections become a major concern for both customers and enterprises. Although data generalization by obfuscation and anonymity can provide protection for an individual’s privacy, overgeneralization may lead to less-valuable data. In this paper, we contrive generalization boundary techniques (k-anonymity) to maximize data usability while minimizing disclosure with a privacy access control mechanism. This paper proposes a combination of purpose-based access control models with an anonymity technique in distributed computing environments for privacy preserving policies and mechanisms that demonstrate policy conflicting problems. This combined approach will provide protections for individual personal information and make data sharable to authorized party with proper purposes. Here, we have examined data with k-anonymity to create a specific level of obfuscation that maintains the usefulness of data and used a heuristic approach to a privacy access control framework in which the privacy requirement is to satisfy the k-anonymity. The extensive experiments on both real-world and synthetic data sets show that the proposed privacy aware access control model with k- anonymity is practical and effective. It will generate an anonymized data set in accordance with the privacy clearance of a certain request and allow users access at different privacy levels, fulfilling some set of obligations and addressing privacy and utility requirements, flexible access control, and improved data availability, while guaranteeing a certain level of privacy.Ope

CA-ARBAC: privacy preserving using context-aware role-based access control on Android permission system

Existing mobile platforms are based on manual way of granting and revoking permissions to applications. Once the user grants a given permission to an application, the application can use it without limit, unless the user manually revokes the permission. This has become the reason for many privacy problems because of the fact that a permission that is harmless at some occasion may be very dangerous at another condition. One of the promising solutions for this problem is context-aware access control at permission level that allows dynamic granting and denying of permissions based on some predefined context. However, dealing with policy configuration at permission level becomes very complex for the user as the number of policies to configure will become very large. For instance, if there are A applications, P permissions, and C contexts, the user may have to deal with A × P × C number of policy configurations. Therefore, we propose a context-aware role-based access control model that can provide dynamic permission granting and revoking while keeping the number of policies as small as possible. Although our model can be used for all mobile platforms, we use Android platform to demonstrate our system. In our model, Android applications are assigned roles where roles contain a set of permissions and contexts are associated with permissions. Permissions are activated and deactivated for the containing role based on the associated contexts. Our approach is unique in that our system associates contexts with permissions as opposed to existing similar works that associate contexts with roles. As a proof of concept, we have developed a prototype application called context-aware Android role-based access control. We have also performed various tests using our application, and the result shows that our model is working as desired

CA-ARBAC: privacy preserving using context-aware role-based access control on Android permission system

Existing mobile platforms are based on manual way of granting and revoking permissions to applications. Once the user grants a given permission to an application, the application can use it without limit, unless the user manually revokes the permission. This has become the reason for many privacy problems because of the fact that a permission that is harmless at some occasion may be very dangerous at another condition. One of the promising solutions for this problem is context-aware access control at permission level that allows dynamic granting and denying of permissions based on some predefined context. However, dealing with policy configuration at permission level becomes very complex for the user as the number of policies to configure will become very large. For instance, if there are A applications, P permissions, and C contexts, the user may have to deal with A × P × C number of policy configurations. Therefore, we propose a context-aware role-based access control model that can provide dynamic permission granting and revoking while keeping the number of policies as small as possible. Although our model can be used for all mobile platforms, we use Android platform to demonstrate our system. In our model, Android applications are assigned roles where roles contain a set of permissions and contexts are associated with permissions. Permissions are activated and deactivated for the containing role based on the associated contexts. Our approach is unique in that our system associates contexts with permissions as opposed to existing similar works that associate contexts with roles. As a proof of concept, we have developed a prototype application called context-aware Android role-based access control. We have also performed various tests using our application, and the result shows that our model is working as desired

Assured information sharing for ad-hoc collaboration

Collaborative information sharing tends to be highly dynamic and often ad hoc among organizations. The dynamic natures and sharing patterns in ad-hoc collaboration impose a need for a comprehensive and flexible approach to reflecting and coping with the unique access control requirements associated with the environment. This dissertation outlines a Role-based Access Management for Ad-hoc Resource Shar- ing framework (RAMARS) to enable secure and selective information sharing in the het- erogeneous ad-hoc collaborative environment. Our framework incorporates a role-based approach to addressing originator control, delegation and dissemination control. A special trust-aware feature is incorporated to deal with dynamic user and trust management, and a novel resource modeling scheme is proposed to support fine-grained selective sharing of composite data. As a policy-driven approach, we formally specify the necessary pol- icy components in our framework and develop access control policies using standardized eXtensible Access Control Markup Language (XACML). The feasibility of our approach is evaluated in two emerging collaborative information sharing infrastructures: peer-to- peer networking (P2P) and Grid computing. As a potential application domain, RAMARS framework is further extended and adopted in secure healthcare services, with a unified patient-centric access control scheme being proposed to enable selective and authorized sharing of Electronic Health Records (EHRs), accommodating various privacy protection requirements at different levels of granularity

Privacy preference mechanisms in Personal Data Storage (PDS).

In this thesis, we study frameworks for managing user's privacy when disclosing personal data with third parties from Personal Data Storage (PDS). PDS is a secure digital space which allows individuals to collect, store, and give access to third parties. So, PDS has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data stored into the PDS. In contrast, this thesis aims at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. This thesis first demonstrates that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. Furthermore, we have revised our first contribution by defining strategies able to obtain good accuracy without requiring too much effort from the user in the training phase. At this aim, we exploit active learning with semi-supervised approach so as to improve the quality of the labeled training dataset. This ables to improve the performance of learning models to predict user privacy preferences correctly. Moreover, in the second part of the thesis we study how user's contextual information play a vital role in term of taking decision of whether to share personal data with third parties. As such, consider that a service provider may provide a request for entertainment service to PDS owner during his/her office hours. In such case, PDS owner may deny this service as he/she is in office. That implies individual would like to accept/deny access requests by considering his/her contextual information. Prior studies on PDS have not considered user's contextual information so far. Moreover, prior research has shown that user privacy preferences may vary based on his/her contextual information. To address this issue, this thesis also focuses to implement a contextual privacy-aware framework for PDS (CP-PDS) which exploits contextual information to build a learning classifier that can predict user privacy preferences under various contextual scenarios. We run several experiments on a realistic dataset and exploiting groups of evaluators. The obtained results show the effectiveness of the proposed approaches

Privacy preference mechanisms in Personal Data Storage (PDS).

In this thesis, we study frameworks for managing user's privacy when disclosing personal data with third parties from Personal Data Storage (PDS). PDS is a secure digital space which allows individuals to collect, store, and give access to third parties. So, PDS has inaugurated a substantial change to the way people can store and control their personal data, by moving from a service-centric to a user-centric model. Up to now, most of the research on PDS has focused on how to enforce user privacy preferences and how to secure data stored into the PDS. In contrast, this thesis aims at designing a Privacy-aware Personal Data Storage (P-PDS), that is, a PDS able to automatically take privacy-aware decisions on third parties access requests in accordance with user preferences. This thesis first demonstrates that semi-supervised learning can be successfully exploited to make a PDS able to automatically decide whether an access request has to be authorized or not. Furthermore, we have revised our first contribution by defining strategies able to obtain good accuracy without requiring too much effort from the user in the training phase. At this aim, we exploit active learning with semi-supervised approach so as to improve the quality of the labeled training dataset. This ables to improve the performance of learning models to predict user privacy preferences correctly. Moreover, in the second part of the thesis we study how user's contextual information play a vital role in term of taking decision of whether to share personal data with third parties. As such, consider that a service provider may provide a request for entertainment service to PDS owner during his/her office hours. In such case, PDS owner may deny this service as he/she is in office. That implies individual would like to accept/deny access requests by considering his/her contextual information. Prior studies on PDS have not considered user's contextual information so far. Moreover, prior research has shown that user privacy preferences may vary based on his/her contextual information. To address this issue, this thesis also focuses to implement a contextual privacy-aware framework for PDS (CP-PDS) which exploits contextual information to build a learning classifier that can predict user privacy preferences under various contextual scenarios. We run several experiments on a realistic dataset and exploiting groups of evaluators. The obtained results show the effectiveness of the proposed approaches

Medically adaptive role based access control model (MAR-BAC)

The development of technology gives opportunity to reach information in a reasonably short amount of time. Ease of access to information does not only create positive consequences, but also provides an easy way to access to information by unauthorized parties. As a result, the requirement of protecting data from different aspects of security turns into a significant issue of the information systems. Another issue in such systems is safeguarding the access permissions in order not to allow public accesses to private data. Protecting the data from disclosure, tempering or destruction as well as prevention of unauthorized use of any resource are important aspects of the security in medical environments since the medical data is private data. In this thesis, we introduce a novel access control mechanism in order to safeguard privacy of medical data of patients in dynamic environments. Our access control model, called MAR-BAC (Medically Adaptive Role Based Access Control), takes advantages from role-based access control (RBAC) and criticality-aware access control (CAAC). Our original approach allows the medical professionals with di erent roles to be granted access to medical records of patients automatically and without explicit request in case of a medical emergency. In this context, we design secure and privacy aware protocols from initial login to patients' medical data transmission and retrieval by the medical professionals. We mostly take a formal approach in our access control model definitions and procedures. The medical awareness feature of our MAR-BAC model comes from the fact that medical data of the patients are analysed in near real-time. Each such analysis yields automatic updates in the access control rules for the sake of urgent medical attention. We carry out simulation based performance evaluation to determine the delay characteristics of our MAR-BAC model. We also analyse the scalability of the system. Our results show that MAR-BAC scales linearly under moderate system load. Again under moderate load and in a hospital with 500 inpatients, the maximum end-to-end delay to react a medical emergency is less than 12 seconds

A method for privacy-preserving collaborative filtering recommendations

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) “neighbourhood privacy” that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied

Supporting Location Privacy Management through Feedback and Control

Participation in modern, socially-focused digital systems involves a large degree of privacy management, i.e. controlling who may access what information under what circumstances. Effective privacy management (control) requires that mobile systems’ users be able to make informed privacy decisions as their experience and knowledge of a system progresses. By informed, we mean users be aware of the actual information flow. Moreover, privacy preferences vary across the context and it is hard to define privacy policy that reflects the dynamic nature of our lives. This research explores the problem of supporting awareness of information flow and designing usable interfaces for maintaining privacy policies ad-hoc. We borrow from the world of Computer Supported Collaborative Work (CSCW) and propose to incorporate social translucence, a design approach that “supports coherent behaviour by making participants and their activities visible to one another”. We use the characteristics of social translucence, namely visibility, awareness and accountability in order to introduce social norms in spatially dispersed systems. Our research is driven by two questions: (1) how can artifacts from real world social interaction, such as responsibility, be embedded into mobile interaction; and (2) can systems be designed in which both privacy violations and the burden of privacy management is minimized. The contributions of our work are: (1) an implementation of Buddy Tracker, privacy-aware location-sharing application based on the social translucence; (2) the design and evaluation of the concept of real-time feedback as a means of incorporating social translucence in location-sharing scenarios; and finally (3) a novel interface for ad-hoc privacy management called Privacy-Shake. We explore the role of real-time feedback for privacy management in the context of Buddy Tracker. Informed by focus group discussions, interviews, surveys and two field trials of Buddy Tracker we found that when using a system that provided real-time feedback, people were more accountable for their actions and reduced the number of unreasonable location requests. From our observations we develop concrete design guidelines for incorporating real-time feedback into information sharing applications in a manner that ensures social acceptance of the technology