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2022 ◽  
Vol 54 (8) ◽  
pp. 1-37
Author(s):  
M. G. Sarwar Murshed ◽  
Christopher Murphy ◽  
Daqing Hou ◽  
Nazar Khan ◽  
Ganesh Ananthanarayanan ◽  
...  

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e., close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems.


Energy Policy ◽  
2022 ◽  
Vol 161 ◽  
pp. 112756
Author(s):  
Frauke Schallehn ◽  
Konstantina Valogianni
Keyword(s):  

2022 ◽  
Vol 40 (1) ◽  
pp. 1-23
Author(s):  
Jiaxing Shen ◽  
Jiannong Cao ◽  
Oren Lederman ◽  
Shaojie Tang ◽  
Alex “Sandy” Pentland

User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bill Ming Gao ◽  
Matthew Tingchi Liu ◽  
Rongwei Chu

Purpose This paper aims to learn about consumers’ information disclosing patterns in the mobile internet context by investigating how demographic, geographic and psychological factors influence their information disclosing willingness (IDW).Design/methodology/approach Drawing on self-disclosure theory, the authors carried out simple linear regression analyses on a Chinese sample of 10,000 participants.Findings The results revealed that significant gender differences exist between males and females in their IDW in mobile internet context, and females have higher IDW than males do. And the authors also found that first-tier (third tier) citizens have the lowest (highest) IDW in their mobile internet usage.Originality/value This study offers three implications. First, this paper captures the insight of IDW within the mobile internet context, while previous studies mostly focus on the desktop internet context. Second, the results show that females have higher willingness to disclose than males do in the context of mobile internet, which is different from the findings of prior studies that females have higher privacy concerns and lower disclosing willingness in the context of desktop internet. Thirdly, this research introduces city tiers as a new approach to the study of IDW, which is one of the first studies exploring the geographical effect on information privacy.


2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Muhammad Asghar Khan ◽  
Insaf Ullah ◽  
Mohammed H. Alsharif ◽  
Abdulaziz H. Alghtani ◽  
Ayman A. Aly ◽  
...  

Internet of drones (IoD) is a network of small drones that leverages IoT infrastructure to deliver real-time data communication services to users. On the one hand, IoD is an excellent choice for a number of military and civilian applications owing to key characteristics like agility, low cost, and ease of deployment; on the other hand, small drones are rarely designed with security and privacy concerns in mind. Intruders can exploit this vulnerability to compromise the security and privacy of IoD networks and harm the information exchange operation. An aggregate signature scheme is the best solution for resolving security and privacy concerns since multiple drones are connected in IoD networks to gather data from a certain zone. However, most aggregate signature schemes proposed in the past for this purpose are either identity-based or relied on certificateless cryptographic methods. Using these methods, a central authority known as a trusted authority (TA) is responsible for generating and distributing secret keys of every user. However, the key escrow problem is formulated as knowing the secret key generated by the TA. These methods are hampered by key distribution issues, which restrict their applicability in a variety of situations. To address these concerns, this paper presents a certificate-based aggregate signature (CBS-AS) scheme based on hyperelliptic curve cryptography (HECC). The proposed scheme has been shown to be both efficient in terms of computation cost and unforgeable while testing its toughness through formal security analysis.


2022 ◽  
Vol 6 (GROUP) ◽  
pp. 1-26
Author(s):  
Sukeshini A. Grandhi ◽  
Linda Plotnick

This study explores privacy concerns perceived by people with respect to having their DNA tested by direct-to-consumer (DTC) genetic testing companies such as 23andMe and Ancestry.com. Data collected from 510 respondents indicate that those who have already obtained a DTC genetic test have significantly lower levels of privacy and security concerns than those who have not obtained a DTC genetic test. Qualitative data from respondents of both these groups show that the concerns are mostly similar. However, the factors perceived to alleviate privacy concerns are more varied and nuanced amongst those who have obtained a DTC genetic test. Our data suggest that privacy concerns or lack of concerns are based on complex and multiple considerations including data ownership, access control of data and regulatory authorities of social, political and legal systems. Respondents do not engage in a full cost/benefit analysis of having their DNA tested.


Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 649
Author(s):  
David Ferreira ◽  
Samuel Silva ◽  
Francisco Curado ◽  
António Teixeira

Speech is our most natural and efficient form of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy), or health conditions (e.g., laryngectomy), preventing the consideration of audible speech. In this regard, silent speech interfaces (SSI) have been proposed as an alternative, considering technologies that do not require the production of acoustic signals (e.g., electromyography and video). Unfortunately, despite their plentitude, many still face limitations regarding their everyday use, e.g., being intrusive, non-portable, or raising technical (e.g., lighting conditions for video) or privacy concerns. In line with this necessity, this article explores the consideration of contactless continuous-wave radar to assess its potential for SSI development. A corpus of 13 European Portuguese words was acquired for four speakers and three of them enrolled in a second acquisition session, three months later. Regarding the speaker-dependent models, trained and tested with data from each speaker while using 5-fold cross-validation, average accuracies of 84.50% and 88.00% were respectively obtained from Bagging (BAG) and Linear Regression (LR) classifiers, respectively. Additionally, recognition accuracies of 81.79% and 81.80% were also, respectively, achieved for the session and speaker-independent experiments, establishing promising grounds for further exploring this technology towards silent speech recognition.


2022 ◽  
Vol 14 (2) ◽  
pp. 900
Author(s):  
Sabrina Oppl ◽  
Christian Stary

Connectivity is key to the latest technologies propagating into everyday life. Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) applications enable users, machines, and technologically enriched objects (‘Things’) to sense, communicate, and interact with their environment. Albeit making human beings’ lives more comfortable, these systems collect huge quantities of data that may affect human privacy and their digital sovereignty. Engaging in control over individuals by digital means, the data and the artefacts that process privacy-relevant data can be addressed by Self-Determination Theory (SDT) and its established instruments. In this paper, we discuss how the theory and its methodological knowledge can be considered for user-centric privacy management. We set the stage for studying motivational factors to improve user engagement in identifying privacy needs and preserving privacy when utilizing or aiming to adapt CPS or IoT applications according to their privacy needs. SDT considers user autonomy, self-perceived competence, and social relatedness relevant for human engagement. Embodying these factors into a Design Science-based CPS development framework could help to motivate users to articulate privacy needs and adopt cyber-physical technologies for personal task accomplishment.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sarah Hudson ◽  
Yi Liu

PurposeAs mobile apps request permissions from users, protecting mobile users' personal information from being unnecessarily collected and misused becomes critical. Privacy regulations, such as General Data Protection Regulation in the European Union (EU), aim to protect users' online information privacy. However, one’s understanding of whether these regulations effectively make mobile users less concerned about their privacy is still limited. This work aims to study mobile users' privacy concerns towards mobile apps by examining the effects of general and specific privacy assurance statements in China and the EU.Design/methodology/approachDrawing on ecological rationality and heuristics theory, an online experiment and a follow-up validation experiment were conducted in the EU and China to examine the effects of privacy assurance statements on mobile users' privacy concerns.FindingsWhen privacy regulation is presented, the privacy concerns of Chinese mobile users are significantly lowered compared with EU mobile users. This indicates that individuals in the two regions react differently to privacy assurances. However, when a general regulation statement is used, no effect is observed. EU and Chinese respondents remain unaffected by general assurance statements.Originality/valueThis study incorporates notions from fast and frugal heuristics end ecological rationality – where seemingly irrational decisions may make sense in different societal contexts.


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