No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A Framework for Supporting Privacy Preservation Functions in a Mobile Cloud Environment
ResearchGate has not been able to resolve any citations for this publication.
The proliferation of Internet of Things (IoT) and the success of resource-rich cloud services have pushed the data processing horizon towards the edge of the network. This has the potential to address bandwidth costs, and latency, availability and data privacy concerns. Serverless computing, a cloud computing model for stateless and event-driven applications, promises to further improve Quality of Service (QoS) by eliminating the burden of always-on infrastructure through ephemeral containers. Open source serverless frameworks have been introduced to avoid the vendor lock-in and computation restrictions of public cloud platforms and to bring the power of serverless computing to on-premises deployments. In an IoT environment, these frameworks can leverage the computational capabilities of devices in the local network to further improve QoS of applications delivered to the user. However, these frameworks have not been evaluated in a resource-constrained, edge computing environment. In this work we evaluate four open source serverless frameworks, namely, Kubeless, Apache OpenWhisk, OpenFaaS, Knative. Each framework is installed on a bare-metal, single master, Kubernetes cluster. We use the JMeter framework to evaluate the response time, throughput and success rate of functions deployed using these frameworks under different workloads. The evaluation results are presented and open research opportunities are discussed.
Advances in sensing and tracking technology enable location-based applications but they also create signif- icant privacy risks. Anonymity can provide a high de- gree of privacy, save service users from dealing with service providers' privacy policies, and reduce the ser- vice providers' requirements for safeguarding private in- formation. However, guaranteeing anonymous usage of location-based services requires that the precise location information transmitted by a user cannot be easily used to re-identify the subject. This paper presents a mid- dleware architecture and algorithms that can be used by a centralized location broker service. The adaptive al- gorithms adjust the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area. Using a model based on automotive traffic counts and carto- graphic material, we estimate the realistically expected spatial resolution for different anonymity constraints. The median resolution generated by our algorithms is 125 meters. Thus, anonymous location-based requests for urban areas would have the same accuracy currently needed for E-911 services; this would provide sufficient resolution for wayfinding, automated bus routing ser- vices and similar location-dependent services.
Location-based services (LBSs) have increasingly provided by a broad range of devices and applications, but one associated risk is location disclosure. To solve this problem, a commonly method is to adopt K-anonymity in the centralized architecture based on a single trusted anonymizer. However, this strategy may compromise user privacy involving continuous LBSs. In this study, we propose a dual-K mechanism (DKM) to protect the users’ trajectory privacy for continuous LBSs. The proposed DKM method firstly inserted multiple anonymizers between the user and the location service provider (LSP), and K query locations are sent to different anonymizers to achieve K-anonymity. Simultaneously, we combined the dynamic pseudonym and the location selection mechanisms to improve user trajectory privacy. Hence, neither the LSP nor the anonymizer can obtain the user trajectory. Security analyses demonstrates that our proposed scheme can effectively enhance user trajectory privacy protection, and the simulation results prove that the DKM scheme can preserve user trajectory privacy with low overhead on a single anonymizer.
This paper contributes to mobile crowdsourcing applications by developing a privacy preserving framework that enables users to contribute content to the community while controlling their privacy exposure. One fundamental challenge in such applications is how to preserve user privacy, as participants may end up revealing a great deal of user-identified, geo-located data, which can easily unfold user trajectories or sensitive locations (e.g., user's home or work location). In this paper we develop PROMPT, a highly efficient privacy preserving framework that runs locally on mobile devices. PROMPT relies on a novel geometric approximation approach to preserve user privacy, by evaluating the privacy exposure of users before sharing their geo-located data. Our detailed experimental evaluation using real-world datasets illustrates that our approach is effective, practical and has low overhead on smartphones.
A privacy-aware proximity detection service determines if two mobile users are close to each other without requiring them to disclose their exact locations. Existing proposals for such services provide weak privacy, give low accuracy guarantees, incur high communication costs, or lack flexibility in user preferences. We address these shortcomings with a client-server solution for proximity detection, based on encrypted, multi-level partitions of the spatial domain. Our service notifies a user if any friend users enter the user’s specified area of interest, called the vicinity region. This region, in contrast to related work, can be of any shape and can be flexibly changed on the fly. Encryption and blind evaluation on the server ensures strong privacy, while low communication costs are achieved by an adaptive location-update policy. Experimental results show that the flexible functionality of the proposed solution is provided with low communication cost.
A ubiquitous computing environment provides comfortable conditions for anyone to access diverse networks without being concerned about time, place, or device. The Location-Based Service (LBS), for example, provides various convenient services using an individual's location information. However, on the flip side of this convenience, if a user's location information is exposed, it can lead to serious privacy problems. This paper proposes an anonymous communication model that uses an echo agent for LBS to guarantee privacy. Located between the user and the service provider, the echo agent sends both the real user's route and dummy routes to prevent user location information from being grasped and traced by service providers. It maintains an effective data schema to collect, store, and use the users' past routes and dummy routes through the heuristic algorithm. Finally, it develops and applies the policies to manage them. Unlike existing methods, this model provides powerful anonymity because it generates dummy routes which are similar to the real routes of users. In addition, in terms of the simulation result for dummy generation, the proposed model reduces the probability of the wrong node generation to a tenth of the existing model's one. Thus, privacy of user location information can be protected via this method.
Serverless boom or bust? an analysis of economic incentives
- lin