C-NEST: cloudlet based privacy preserving multidimensional data stream approach for healthcare electronics.

The Medical Internet of Things (MIoT) facilitates extensive connections between cyber and physical "things" allowing for effective data fusion and remote patient diagnosis and monitoring. However, there is a risk of incorrect diagnosis when data is tampered with from the cloud or a hospital due to third-party storage services. Most of the existing systems use an owner-centric data integrity verification mechanism, which is not computationally feasible for lightweight wearable-sensor systems because of limited computing capacity and privacy leakage issues. In this regard, we design a 2-step Privacy-Preserving Multidimensional Data Stream (PPMDS) approach based on a cloudlet framework with an Uncertain Data-integrity Optimization (UDO) model and Sparse-Centric SVM (SCS) model. The UDO model enhances health data security with an adaptive cryptosystem called Cloudlet-Nonsquare Encryption Secret Transmission (C-NEST) strategy by avoiding medical disputes during data streaming based on novel signature and key generation strategies. The SCS model effectively classifies incoming queries for easy access to data by solving scalability issues. The cloudlet server measures data integrity and authentication factors to optimize third-party verification burden and computational cost. The simulation outcomes show that the proposed system optimizes average data leakage error rate by 27%, query response time and average data transmission time are reduced by 31%, and average communication-computation cost are reduced by 61% when measured against state-of-the-art approaches

User-Centric Security and Privacy Mechanisms in Untrusted Networking and Computing Environments

Our modern society is increasingly relying on the collection, processing, and sharing of digital information. There are two fundamental trends: (1) Enabled by the rapid developments in sensor, wireless, and networking technologies, communication and networking are becoming more and more pervasive and ad hoc. (2) Driven by the explosive growth of hardware and software capabilities, computation power is becoming a public utility and information is often stored in centralized servers which facilitate ubiquitous access and sharing. Many emerging platforms and systems hinge on both dimensions, such as E-healthcare and Smart Grid. However, the majority information handled by these critical systems is usually sensitive and of high value, while various security breaches could compromise the social welfare of these systems. Thus there is an urgent need to develop security and privacy mechanisms to protect the authenticity, integrity and confidentiality of the collected data, and to control the disclosure of private information. In achieving that, two unique challenges arise: (1) There lacks centralized trusted parties in pervasive networking; (2) The remote data servers tend not to be trusted by system users in handling their data. They make existing security solutions developed for traditional networked information systems unsuitable. To this end, in this dissertation we propose a series of user-centric security and privacy mechanisms that resolve these challenging issues in untrusted network and computing environments, spanning wireless body area networks (WBAN), mobile social networks (MSN), and cloud computing. The main contributions of this dissertation are fourfold. First, we propose a secure ad hoc trust initialization protocol for WBAN, without relying on any pre-established security context among nodes, while defending against a powerful wireless attacker that may or may not compromise sensor nodes. The protocol is highly usable for a human user. Second, we present novel schemes for sharing sensitive information among distributed mobile hosts in MSN which preserves user privacy, where the users neither need to fully trust each other nor rely on any central trusted party. Third, to realize owner-controlled sharing of sensitive data stored on untrusted servers, we put forward a data access control framework using Multi-Authority Attribute-Based Encryption (ABE), that supports scalable fine-grained access and on-demand user revocation, and is free of key-escrow. Finally, we propose mechanisms for authorized keyword search over encrypted data on untrusted servers, with efficient multi-dimensional range, subset and equality query capabilities, and with enhanced search privacy. The common characteristic of our contributions is they minimize the extent of trust that users must place in the corresponding network or computing environments, in a way that is user-centric, i.e., favoring individual owners/users

How Privacy-Enhanced Technologies (Pets) are Transforming Digital Healthcare Delivery

Privacy Enhancing Technologies (PETs) are playing a crucial role in maturing digital healthcare delivery for mainstream adaption from both a social and regulatory perspective. Different PETs are improving different aspects of digital healthcare delivery, and we have chosen seven of them to observe in the context of their influence on digital healthcare and their use cases. Homomorphic encryption can provide data security when healthcare data is being collected from individuals via IoT or IoMT devices. It’s also a key facilitator for large-scale healthcare data pooling from multiple sources for analytics without compromising privacy. Secure Multi-Party Computation (SMPC) facilitates safe data transfer between patients and healthcare professionals, and other relevant entities. Generative Adversarial Networks (GANs) can be used to generate larger data sets from smaller training data sets directly obtained from the patients, to train AI and ML algorithms. Differential Privacy (DP) focuses on combining multiple data sets for collective or individual processing without compromising privacy. However, its addition of noise to obscure data has some technical limitations. Zero-Knowledge Proof (ZKP) can facilitate safe verifications/validation protocols to establish connections between healthcare devices without straining their hardware capacities. Federated learning leans quite heavily towards training AI/ML algorithms on multiple data sets without margining or compromising the privacy of the constituents of any dataset. Obfuscation can be used in different stages of healthcare delivery to obscure healthcare data.