An Automated Recognition Model for Sensitive Information

BACKGROUND Geographic momentary assessments (GMA) collect real-time behavioral data in one’s natural environment using a smartphone and could potentially increase the ecological validity of behavioral data. Several studies have evaluated the feasibility and acceptability of GMA among persons who use drugs (PWUD) and men who have sex with men (MSM), but fewer have discussed privacy, confidentiality, and safety concerns, particularly when illegal or stigmatized behavioral data were collected. OBJECTIVE This study explores perceptions regarding privacy, confidentiality, and safety of GMA research among PWUD and MSM recruited in three different settings (rural Appalachia, a mid-sized city in the South, and a mid-Atlantic city). METHODS Between November 2014 and April 2017 we recruited 35 PWUD from rural Appalachian Kentucky (N=20) and Baltimore, Maryland (N=15), and 20 MSM from Lexington, Kentucky to complete semi-structured qualitative interviews. Through thematic analyses, we identified and compared privacy, confidentiality, and safety concerns by demographic characteristics, risk behaviors, and setting. RESULTS Privacy, confidentiality, and safety concerns varied by setting, age, smartphone ownership, use of illegal drugs, and history of drug-related arrests. Among those who used drugs, participants reported concerns with being tracked and burden associated with carrying and safeguarding study phones and responding to survey prompts. Privacy and confidentiality concerns were noted in each setting, but tracking concerns were greatest among Baltimore participants and led many to feel that they (or others) would be unwilling to participate or comply with study procedures. While locations considered to be sensitive varied by setting, participants in all settings said they would take measures to prevent sensitive information from being collected (i.e. intentionally disable devices, leave phones at home, alter response times). CONCLUSIONS Privacy, confidentiality, and safety concerns may limit the accuracy of risk location information, study compliance, and participation. As concerns were often greatest among those engaging in illegal behaviors and with the highest risk behaviors, selection bias and non-response bias could negatively influence the representativeness and validity of study findings.

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Automated recognition of jumps in GOES satellite magnetic data

Russian Journal of Earth Sciences ◽

10.2205/2018es000626 ◽

2018 ◽

Vol 18 (4) ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

A. Soloviev ◽

Sh. Bogoutdinov ◽

S. Agayan ◽

R. Redmon ◽

T. M. Loto'aniu ◽

...

Keyword(s):

Magnetic Data ◽

Automated Recognition

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Subject-Dependent Physical Activity Recognition Model Framework with a Semi-supervised Clustering Approach

2013 European Modelling Symposium ◽

10.1109/ems.2013.7 ◽

2013 ◽

Cited By ~ 2

Author(s):

Hashim Ali ◽

Enza Messina ◽

Roberto Bisiani

Keyword(s):

Physical Activity ◽

Activity Recognition ◽

Model Framework ◽

Recognition Model ◽

Supervised Clustering ◽

Clustering Approach ◽

Physical Activity Recognition

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Secure multiparty quantum key agreement against collusive attacks

Scientific Reports ◽

10.1038/s41598-021-88837-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hussein Abulkasim ◽

Atefeh Mashatan ◽

Shohini Ghose

Keyword(s):

Key Agreement ◽

Sensitive Information ◽

Quantum Key Agreement ◽

Key Agreement Protocol ◽

Proposed Model ◽

Shared Key ◽

Agreement Protocols ◽

Collusive Attacks ◽

Attack Strategy ◽

Collusive Attack

AbstractQuantum key agreement enables remote participants to fairly establish a secure shared key based on their private inputs. In the circular-type multiparty quantum key agreement mode, two or more malicious participants can collude together to steal private inputs of honest participants or to generate the final key alone. In this work, we focus on a powerful collusive attack strategy in which two or more malicious participants in particular positions, can learn sensitive information or generate the final key alone without revealing their malicious behaviour. Many of the current circular-type multiparty quantum key agreement protocols are not secure against this collusive attack strategy. As an example, we analyze the security of a recently proposed multiparty key agreement protocol to show the vulnerability of existing circular-type multiparty quantum key agreement protocols against this collusive attack. Moreover, we design a general secure multiparty key agreement model that would remove this vulnerability from such circular-type key agreement protocols and describe the necessary steps to implement this model. The proposed model is general and does not depend on the specific physical implementation of the quantum key agreement.

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