5,387 research outputs found
Health Information Systems in the Digital Health Ecosystem—Problems and Solutions for Ethics, Trust and Privacy
Digital health information systems (DHIS) are increasingly members of ecosystems, collecting, using and sharing a huge amount of personal health information (PHI), frequently without control and authorization through the data subject. From the data subject's perspective, there is frequently no guarantee and therefore no trust that PHI is processed ethically in Digital Health Ecosystems. This results in new ethical, privacy and trust challenges to be solved. The authors' objective is to find a combination of ethical principles, privacy and trust models, together enabling design, implementation of DHIS acting ethically, being trustworthy, and supporting the user's privacy needs. Research published in journals, conference proceedings, and standards documents is analyzed from the viewpoint of ethics, privacy and trust. In that context, systems theory and systems engineering approaches together with heuristic analysis are deployed. The ethical model proposed is a combination of consequentialism, professional medical ethics and utilitarianism. Privacy enforcement can be facilitated by defining it as health information specific contextual intellectual property right, where a service user can express their own privacy needs using computer-understandable policies. Thereby, privacy as a dynamic, indeterminate concept, and computational trust, deploys linguistic values and fuzzy mathematics. The proposed solution, combining ethical principles, privacy as intellectual property and computational trust models, shows a new way to achieve ethically acceptable, trustworthy and privacy-enabling DHIS and Digital Health Ecosystems
Privacy-preserving outsourced support vector machine design for secure drug discovery
AXA Research Fund, Singapore Management Universit
BALANCED AWARE FIREFLY OPTIMIZATION BASED COST-EFFECTIVE PRIVACY PRESERVING APPROACH OF INTERMEDIATE DATA SETS OVER CLOUD COMPUTING
Cloud computing is an embryonic archetype with remarkable impetus; however its exclusive facets intensify safety and privacy confronts. In the previous method, the privacy of intermediate data set problems is dealt with which is concentrated to regain privacy sensitive information. Alternatively the previous system contains problem with time and cost intricacy. As well it contains issue with dealing privacy conscious well-organized scheduling of intermediate data sets in cloud by considering privacy preserving. In order to surmount the above stated problems, in the existing system, enhanced balanced scheduling methodology is presented to get better the cost complexity and privacy preservation. Balanced aware FireFly Optimization (BFFO) is used for proficient privacy conscious data set scheduling. This technique is utilized to discover the resolution that carries out best on poise amongst a set of resolutions with similar execution time. Consequently the research system gives superior privacy preservation and enhanced scheduling cost more willingly than the previous method. The encryption technique is used to guarantee the security and end users decrypted the real information with improved privacy. The experimentation outcome show that the presented method confirms superior privacy, lesser cost, lesser time complexity and proficient storage metrics utilizing BFFO methodology compared to the previous Cost based Heuristic (C_HEU) algorithm
CSM-H-R: An Automatic Context Reasoning Framework for Interoperable Intelligent Systems and Privacy Protection
Automation of High-Level Context (HLC) reasoning for intelligent systems at
scale is imperative due to the unceasing accumulation of contextual data in the
IoT era, the trend of the fusion of data from multi-sources, and the intrinsic
complexity and dynamism of the context-based decision-making process. To
mitigate this issue, we propose an automatic context reasoning framework
CSM-H-R, which programmatically combines ontologies and states at runtime and
the model-storage phase for attaining the ability to recognize meaningful HLC,
and the resulting data representation can be applied to different reasoning
techniques. Case studies are developed based on an intelligent elevator system
in a smart campus setting. An implementation of the framework - a CSM Engine,
and the experiments of translating the HLC reasoning into vector and matrix
computing especially take care of the dynamic aspects of context and present
the potentiality of using advanced mathematical and probabilistic models to
achieve the next level of automation in integrating intelligent systems;
meanwhile, privacy protection support is achieved by anonymization through
label embedding and reducing information correlation. The code of this study is
available at: https://github.com/songhui01/CSM-H-R.Comment: 11 pages, 8 figures, Keywords: Context Reasoning, Automation,
Intelligent Systems, Context Modeling, Context Dynamism, Privacy Protection,
Context Sharing, Interoperability, System Integratio
SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search
The -Nearest Neighbor Search (-NNS) is the backbone of several
cloud-based services such as recommender systems, face recognition, and
database search on text and images. In these services, the client sends the
query to the cloud server and receives the response in which case the query and
response are revealed to the service provider. Such data disclosures are
unacceptable in several scenarios due to the sensitivity of data and/or privacy
laws.
In this paper, we introduce SANNS, a system for secure -NNS that keeps
client's query and the search result confidential. SANNS comprises two
protocols: an optimized linear scan and a protocol based on a novel sublinear
time clustering-based algorithm. We prove the security of both protocols in the
standard semi-honest model. The protocols are built upon several
state-of-the-art cryptographic primitives such as lattice-based additively
homomorphic encryption, distributed oblivious RAM, and garbled circuits. We
provide several contributions to each of these primitives which are applicable
to other secure computation tasks. Both of our protocols rely on a new circuit
for the approximate top- selection from numbers that is built from comparators.
We have implemented our proposed system and performed extensive experimental
results on four datasets in two different computation environments,
demonstrating more than faster response time compared to
optimally implemented protocols from the prior work. Moreover, SANNS is the
first work that scales to the database of 10 million entries, pushing the limit
by more than two orders of magnitude.Comment: 18 pages, to appear at USENIX Security Symposium 202
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