Confidentiality of Statistical Records: A Threat-Monitoring Scheme for On Line Dialogue

1976 ◽  
Vol 15 (01) ◽  
pp. 36-42 ◽  
Author(s):  
J. Schlörer
Keyword(s):  
Statistical Data ◽  
Cost Benefit ◽  
Data Bank ◽  
High Ratio ◽  
Point Of View ◽  
Data Sets ◽  
Monitoring Scheme ◽  
Access Controls ◽  
On Line ◽  
Bona Fide

From a statistical data bank containing only anonymous records, the records sometimes may be identified and then retrieved, as personal records, by on line dialogue. The risk mainly applies to statistical data sets representing populations, or samples with a high ratio n/N. On the other hand, access controls are unsatisfactory as a general means of protection for statistical data banks, which should be open to large user communities. A threat monitoring scheme is proposed, which will largely block the techniques for retrieval of complete records. If combined with additional measures (e.g., slight modifications of output), it may be expected to render, from a cost-benefit point of view, intrusion attempts by dialogue valueless, if not absolutely impossible. The bona fide user has to pay by some loss of information, but considerable flexibility in evaluation is retained. The proposal of controlled classification included in the scheme may also be useful for off line dialogue systems.

scholarly journals Categorizing Touch-Input Locations from Touchscreen Device Interfaces via On-Board Mechano-Acoustic Transducers

2021 ◽  
Vol 11 (11) ◽  
pp. 4834
Author(s):  
Kai Ren Teo ◽  
Balamurali B T ◽  
Jianying Zhou ◽  
Jer-Ming Chen
Keyword(s):  
User Interaction ◽  
Data Access ◽  
Machine Learning Techniques ◽  
Acoustic Features ◽  
Touch Input ◽  
Acoustic Transducers ◽  
Learning Techniques ◽  
Access Controls ◽  
Input Location ◽  
Mitigation Methods

Many mobile electronics devices, including smartphones and tablets, require the user to interact physically with the device via tapping the touchscreen. Conveniently, these compact devices are also equipped with high-precision transducers such as accelerometers and microphones, integrated mechanically and designed on-board to support a range of user functionalities. However, unintended access to these transducer signals (bypassing normal on-board data access controls) may allow sensitive user interaction information to be detected and thereby exploited. In this study, we show that acoustic features extracted from the on-board microphone signals, supported with accelerometer and gyroscope signals, may be used together with machine learning techniques to successfully determine the user’s touch input location on a touchscreen: our ensemble model, namely the random forest model, predicts touch input location with up to 86% accuracy in a realistic scenario. Accordingly, we present the approach and techniques used, the performance of the model developed, and also discuss limitations and possible mitigation methods to thwart possible exploitation of such unintended signal channels.

PICS: Internet access controls without censorship

1996 ◽  
Vol 39 (10) ◽  
pp. 87-93 ◽  
Author(s):  
Paul Resnick ◽  
James Miller
Keyword(s):  
Internet Access ◽  
Access Controls

Inspecting Access Controls in Cloud Based Web Application

2018 ◽  
Author(s):  
N. CH. Ravi ◽  
M. Naresh Babu ◽  
R. Sridevi ◽  
V. Kamakshi Prasad ◽  
A. Govardhan ◽  
...  
Keyword(s):  
Web Application ◽  
Access Controls

Overview of Authentication and Access Controls for I&C systems

2018 ◽  
Author(s):  
Asmaa Tellabi ◽  
Jochen Sassmanhausen ◽  
Edita Bajramovic ◽  
Karl Christoph Ruland
Keyword(s):  
Access Controls

scholarly journals Data safe havens to combine health and genomic data: benefits and challenges

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerina H Jones ◽  
Arron S Lacey ◽  
Brian L Perkins ◽  
Mark I Rees
Keyword(s):  
Association Studies ◽  
Genomic Data ◽  
Population Level ◽  
Data Availability ◽  
Genome Wide Association Studies ◽  
Related Data ◽  
Research Areas ◽  
Individual Privacy ◽  
Access Controls ◽  
Health Related

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely. ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data. ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals. ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

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