Worker Privacy Research Articles

PPAT: An effective scheme ensuring privacy-preserving, accuracy, and trust for worker selection in mobile crowdsensing networks

The data content privacy protection and data accuracy are two important research issues in Mobile Crowdsensing (MCS). However, current researches have rarely been able to satisfy privacy protection as well as data accuracy at the same time, thus hindering the development of MCS. To solve the above issues, for the first time, we have proposed a Privacy Preserving, Accuracy, and Trust data collection scheme (PPAT) for MCS, which can protect the privacy of data content and maintain high accuracy at low-cost style. In PPAT scheme, First, we proposed a scrambled data privacy protection framework which can protect the data of each worker from being known to any third party, which can protect the data privacy of workers. The second, more importantly, we propose a truth value estimation method based on trust computing, which can obtain the truth value more accurately compared to the classic methods under privacy-preserving. In the proposed trust-based truth value calculation, the worker's trust is determined by comparing it with the weight of the trusted worker. Then, the truth value is calculated by the trust of the workers, so that the truth value obtained is more accurate. Through theoretical analysis, it is proved that the proposed PPAT scheme has good worker data content, worker trust, and truth value content privacy protection. Through a large number of simulation experiments, the strategy proposed in this paper has a good ability to protect data content privacy compared to the previous strategy, while improving data quality by 0.5%∼5.7%, and reducing data collection costs by 35.6%∼54.9%.

Optimizing task allocation with temporal‐spatial privacy protection in mobile crowdsensing

AbstractMobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.

A Quality-Aware and Obfuscation-Based Data Collection Scheme for Cyber-Physical Metaverse Systems

In pursuit of an immersive virtual experience within the Cyber-Physical Metaverse Systems (CPMS), the construction of Avatars often requires a significant amount of real-world data. Mobile Crowd Sensing (MCS) has emerged as an efficient method for collecting data for CPMS. While progress has been made in protecting the privacy of workers, little attention has been given to safeguarding task privacy, potentially exposing the intentions of applications and posing risks to the development of the Metaverse. Additionally, existing privacy protection schemes hinder the exchange of information among entities, inadvertently compromising the quality of the collected data. To this end, we propose a Quality-aware and Obfuscation-based Task Privacy-Preserving (QOTPP) scheme, which protects task privacy and enhances data quality without third-party involvement. The QOTPP scheme initially employs the insight of “showing the fake, and hiding the real” by employing differential privacy techniques to create fake tasks and conceal genuine ones. Additionally, we introduce a two-tier truth discovery mechanism using Deep Matrix Factorization (DMF) to efficiently identify high-quality workers. Furthermore, we propose a Combinatorial Multi-Armed Bandit (CMAB)-based worker incentive and selection mechanism to improve the quality of data collection. Theoretical analysis confirms that our QOTPP scheme satisfies essential properties such as truthfulness, individual rationality, and ϵ-differential privacy. Extensive simulation experiments validate the state-of-the-art performance achieved by QOTPP.

APPLICATION OF ADVANCED TECHNOLOGIES IN AGRICULTURAL SECTOR FOR INCREASING GROSS PRODUCTION AND EXPORTING AGRICULTURAL PRODUCTS

This scientific article examines the influence of information technologies on the agro-industrial complex (AIC) during armed conflicts, specifically using the example of the war in Ukraine. Military actions have a negative impact on agricultural activities, leading to the destruction of crops, railways, transportation blockades, and infrastructure. However, information technologies can play a crucial role in ensuring food security and supporting the AIC in the context of armed conflict.The article analyzes the utilization of information technologies such as remote sensing of Earth, geographic information systems, drones, and agricultural sensors, which can provide essential information about the state of crops, water resources, and land areas. These technologies assist farmers in planning and making informed decisions regarding optimal resource utilization and ensuring food security even during times of armed conflict. Furthermore, the article examines the effectiveness of mobile applications, electronic document management, and farm management systems in ensuring efficient production and organizing agricultural processes to maintain the sustainable functioning of the AIC during armed conflict. These technologies enable farmers and agricultural enterprise managers to conduct effective monitoring and resource management, plan farm operations, and coordinate personnel actions even in the face of military threats. Additionally, the article analyzes the ethical aspects of using information technologies in the AIC during armed conflicts. This includes issues of data confidentiality protection for farmers, ethical use of data from mobile applications, and the safeguarding of worker privacy. The impact of these technologies on society and rural development during conflicts is also explored. Overall, the article provides a comprehensive view of the use of information technologies in agriculture during armed conflict and emphasizes the necessity of developing and implementing specialized solutions that address the unique challenges and needs of the AIC in such exceptional circumstances. Research in this field can contribute to ensuring the sustainable development of agriculture and enhancing the resilience of the AIC to armed conflicts and other hazards.

Privacy Self‐Management: A Strategy to Protect Worker Privacy from Excessive Employer Surveillance in Light of Scant Legal Protections

This article examines current and future trends in worker surveillance. It also examines the various, though minimal, legal protections workers have against extensive work‐related monitoring. With no meaningful legal protections against excessive work‐related surveillance, employees are arguably taking matters into their own hands by engaging in deviant behaviors that attempt to thwart surveillance efforts. Factoring in the ethical and managerial dimensions of a workforce under constant and excessive surveillance, this article examines a way forward for workers to engage in self‐managed privacy, potentially leading to a less intrusive, but still productive, work environment.

Privacy-preserving mobile crowd sensing task assignment with Stackelberg game

Mobile Crowd Sensing (MCS) has emerged as an effective method for sensing and collecting large-scale computational data in real time, which is favored by researchers from a wide range of industries. Task assignment is an important research direction in mobile crowd sensing. In the scenario of multiple workers and multiple tasks, it is a key challenge to develop an excellent task assignment scheme to ensure that both the platform and workers can also get high utility while workers’ location privacy is not leaked. In this regard, this paper proposes a task assignment scheme (PCTA-SG) with worker location privacy protection and worker merit-based selection mechanism, which improves platform utility and equilibrates worker utility through worker path planning and Stackelberg game under the constraint of a limited perceived budget. First, the platform publishes the task set and its related information. Next, each candidate worker perturbs its own location locally based on geographical indistinguishability, and uploads the required information to the platform after selecting the tasks. Then a worker priority factor model is established by comprehensively considering the factors such as worker credibility, perceived device power and so on. According to the priority factors of workers, the platform uses a greedy strategy to select high-quality and suitable workers and assign tasks to them, thereby forming an initial solution for task assignment. To enhance the utility of the platform, the initial solution for task assignment is continued as the initial population of the genetic algorithm to optimize the moving distance of workers and generate the final solution for task assignment. Finally, on the basis of the final solution for task assignment, a two-stage Stackelberg game is carried out between the platform and workers to further optimize the utility of the platform and take into account the utility of workers, thus perfecting the task assignment scheme. The experimental evaluation results with other baseline schemes under real-world dataset show that the scheme in this paper achieves the best overall effect. It not only protects the location privacy of workers, but also achieves good worker utility while obtaining the optimal platform utility and platform input–output ratio.

From access to understanding: Collective data governance for workers

Regulating data collection and use in the workplace is now more a matter of regulating working conditions than data protection. This article argues that existing data protection law fails workers for precisely this reason. We examine how workers currently use data protection law, labour law, and technology to access and leverage the data they produce at work and identify key ways in which current regulation falls short. Existing regulations primarily aim to protect worker privacy, an approach that ignores the fact that data use now often defines the fundamental conditions of work, particularly in the gig economy. This is because a key limitation of modern data protection law for workers is its myopic focus on the individual ‘data subject’, whose rights to data stem from a right to privacy or data protection. Instead, data regulation in the workplace requires a framework that acknowledges the core interest workers have in accessing their data: to collectively exert greater agency and control at work. We argue that workplace data regulation should largely be a matter of workplace governance and worker co-determination, an approach rooted in workers’ rights, to negotiate the terms of their employment agreements and specific working environments.

PPTA: A location privacy-preserving and flexible task assignment service for spatial crowdsourcing

With the rapid growth of sensor-rich mobile devices, spatial crowdsourcing (SC) has emerged as a new crowdsourcing paradigm harnessing the crowd to perform location-dependent tasks. To appropriately select workers that are near the tasks, SC systems need to perform location-based task assignment, which requires collecting worker locations and task locations. Such practice, however, may easily compromise the location privacy of workers. In light of this, in this paper, we design, implement, and evaluate PPTA, a new system framework for location privacy-preserving task assignment in SC with strong security guarantees. PPTA takes advantage of only lightweight cryptography (such as additive secret sharing, function secret sharing, and secure shuffle), and provides a suite of tailored secure components required by practical location-based task assignment processes. Specifically, aiming for practical usability, PPTA is designed to flexibly support two realistic task assignment settings: (i) the online setting where tasks arrive and get processed at the SC platform one by one, and (ii) the batch-based setting where tasks arrive and get processed in a batch. Extensive experiments over a real-world dataset demonstrate that while providing strong security guarantees, PPTA supports task assignment with efficacy comparable to plaintext baselines and with promising performance.

A Personalized Privacy Preserving Mechanism for Crowdsourced Federated Learning

In this paper, we focus on the privacy preserving mechanism design for crowdsourced Federated Learning (FL), where a requester can outsource its model training task to some workers via an FL platform. A potential way to preserve the privacy of workers' local data is to leverage Differential Privacy (DP) mechanisms on local models. However, most of these studies cannot allow workers to dominate their own privacy protection levels by themselves. Thus, we propose a Personalized Privacy Preserving Mechanism, called P3M, to satisfy the heterogeneous privacy needs of workers, which consists of two parts. The first part includes a personalized privacy budget determination problem. We model it as a two-stage Stackelberg game, derive the personalized privacy budget for each worker and the optimal payment for the requester, and prove that they form a unique Stackelberg equilibrium. Second, we design a dynamic perturbation scheme to perturb model parameters. Through the theoretical analysis, we prove that P3M satisfies the desired DP property, and derive the bounds of the variance of average perturbed parameters and the convergence upper bound. This demonstrates that the global model accuracy can be controllable and P3M is endowed with the satisfactory convergence performance. In addition, we extend our problem to the scenario where the total privacy budget of all workers is limited, so as to prevent some workers from setting exorbitant privacy budgets. Under the privacy constraint, we re-determine the personalized privacy budget for each worker. Finally, exhaustive simulations of P3M are conducted based on real-world datasets, and the experimental results corroborate its effectiveness and practicability.

Negotiating limits on algorithmic management in digitalised services: cases from Germany and Norway

Artificial intelligence (AI)-based algorithms are increasingly used to monitor employees and to automate management decisions. In this article, we ask how worker representatives adapt traditional collective voice institutions to regulate the adoption and use of these tools in the workplace. Our findings are based on a comparative study of union and works council responses to algorithmic management in contact centres from two similar telecommunications companies in Germany and Norway. In both case studies, worker representatives mobilised collective voice institutions to protect worker privacy and discretion associated with remote monitoring and workforce management technologies. However, they relied on different sources of institutional power, connected to co-determination rights, enforcement of data protection laws, and labour cooperation structures.

Monitoring Worker Exposure to COVID-19 and Other Occupational Risks Using BLE Beacons

The COVID-19 pandemic has become a public health priority during 2020. Social safety distance is one of the most effective strategies to stop the spreading of the virus, as it reduces the dose of infectious particles that a person can receive. Real-time location systems (RTLS) based on ultrawideband (UWB), radio frequency identification (RFID), Global Position System (GPS), or Bluetooth Low Energy (BLE) can help keep workers safe at the workplace. The aim of the current paper is to develop a dosimeter proposal to monitor and control the distance and exposure time between workers based on BLE beacon technology considering viral load. Our proposal is based on a set of BLE beacons and safety distance estimation by filtering RSSI measurements with a Gaussian extended Kalman filter. According to the estimated proximity values and the exposure time, a finite state machine will alarm when the worker receives the maximum dose defined by health authorities. The proposed system can be applied to prevent any risk that can be eliminated or reduced controlling distances and/or exposition time of the worker to the occupational risk. The proposal is robust, is inexpensive, and respects the privacy of workers, and its accuracy is higher than that of existing smartphone applications. In future pandemic situations, the system can be easily updated to the safety distance and viral particle dose related with the new risk agent. The system can protect from additional risk incorporating beacons on the extra risk identified such as thermal, noise, or radiation.

Weaving Privacy and Power

We investigate the privacy practices of labor organizers in the computing technology industry and explore the changes in these practices as a response to remote work. Our study is situated at the intersection of two pivotal shifts in workplace dynamics: (a) the increase in online workplace communications due to remote work, and (b) the resurgence of the labor movement and an increase in collective action in workplaces-especially in the tech industry, where this phenomenon has been dubbed the tech worker movement. The shift of work-related communications to online digital platforms in response to an increase in remote work is creating new opportunities for and risks to the privacy of workers. These risks are especially significant for organizers of collective action, with several well-publicized instances of retaliation against labor organizers by companies. Through a series of qualitative interviews with 29 tech workers involved in collective action, we investigate how labor organizers assess and mitigate risks to privacy while engaging in these actions. Among the most common risks that organizers experienced are retaliation from their employer, lateral worker conflict, emotional burnout, and the possibility of information about the collective effort leaking to management. Depending on the nature and source of the risk, organizers use a blend of digital security practices and community-based mechanisms. We find that digital security practices are more relevant when the threat comes from management, while community management and moderation are central to protecting organizers from lateral worker conflict. Since labor organizing is a collective rather than individual project, individual privacy and collective privacy are intertwined, sometimes in conflict and often mutually constitutive. Notions of privacy that solely center individuals are often incompatible with the needs of organizers, who noted that safety in numbers could only be achieved when workers presented a united front to management. Based on our interviews, we identify key topics for future research, such as the growing prevalence of surveillance software and the needs of international and gig worker organizers.We conclude with design recommendations that can help create safer, more secure and more private tools to better address the risks that organizers face.

Overview and commentary of the California Workplace Technology Accountability Act

ABSTRACT Technological innovation and the resulting automation are increasingly being applied in the workplace, which is a high-risk context where decisions can majorly impact a worker’s life. Consequently, a number of ethical concerns with automated employment decision systems have been raised, with California proposing the Workplace Technology Accountability Act to limit the use of electronic monitoring systems and automated decision systems to specific times of day, activities and locations that must be proven as essential job functions. Workers would also be given the right to know, review and correct data held about them by their employer. In this article, we summarise and discuss the key points of the legislation before providing a commentary, where we identify four key themes: (i) how boundaries can contribute to a healthy work-life balance and protect the privacy of workers; (ii) how the requirement for impact assessments reflects the wider movement towards algorithmic assurance; (iii) the necessary and potentially problematic requirement to share notices and impact assessment reports with the Labor Agency; and (iv) how the Act might conflict with existing laws. Our intended readership is those interested in the regulation of automated employment decision tools, algorithmic assurance, and the potential impact of the proposed legislation.

A novel platform to enable the future human-centered factory

This paper introduces a web-platform system that performs semi-automatic compute of several risk indexes, based on the considered evaluation method (e.g., RULA—Rapid Upper Limb Assessment, REBA—Rapid Entire Body Assessment, OCRA—OCcupational Repetitive Action) to support ergonomics risk estimation, and provides augmented analytics to proactively improve ergonomic risk monitoring based on the characteristics of workers (e.g., age, gender), working tasks, and environment. It implements a body detection system, marker-less and low cost, based on the use of RGB cameras, which exploits the open-source deep learning model CMU (Carnegie Mellon University), from the tf-pose-estimation project, assuring worker privacy and data protection, which has been already successfully assessed in standard laboratory conditions. The paper provides a full description of the proposed platform and reports the results of validation in a real industrial case study regarding a washing machine assembly line composed by 5 workstations. A total of 15 workers have been involved. Results suggest how the proposed system is able to significantly speed up the ergonomic assessment and to predict angles and perform a RULA and OCRA analysis, with an accuracy comparable to that obtainable from a manual analysis, even under the unpredictable conditions that can be found in a real working environment.

A Blockchain Based Privacy-Preserving Incentive Mechanism for Internet of Vehicles in Satellite-Terrestrial Crowdsensing

Crowdsensing has been a popular technology recently and has broad application prospects. The Internet of Vehicles (IoV) refers to the effective utilization of static and dynamic information on the network platform through electronic equipment loaded on vehicles and wireless technology, which means there are many devices generating a huge amount of worthy data. However, preserving the privacy of workers and incentivizing them should be concerned. To this end, we apply the satellite-terrestrial system to solve the communication limitation among vehicles. We propose to use blockchain to act as the channel that requesters and vehicles communicate in. Smart contracts are the main designable parts to do the interactions among the participants. Blockchain is tightly related to payment, so that we implement a total payment minimized auction algorithm on it. We carry out extensive simulations and run an experiment on blockchain to find the auction can be implemented as a smart contract and achieve privacy protection.

Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles

Nowadays, task allocation has attracted increasing attention in the Internet of Vehicles. To efficiently allocate tasks to suitable workers, users usually need to publish their task interests to the service provider, which brings a serious threat to users' privacy. Existing task allocation schemes either cannot comprehensively preserve user privacy (i.e., requester privacy and worker privacy) or introduce tremendous resource overhead. In this paper, we propose an efficient and privacy-preserving versatile task allocation scheme (PPVTA) for the Internet of vehicles. Specifically, we utilize the randomizable matrix multiplication technique to preserve requester privacy and worker privacy. Then, the polynomial fitting technique is leveraged to enrich the randomizable matrix multiplication to support versatile task allocation functions, such as threshold-based task allocation (PPVTA-I), conjunctive task allocation (PPVTA-II), and task allocation with bilateral access control (PPVTA-III). We formally analyze the security of our constructions to prove the security under the chosen-plain attack. Based on a prototype, experimental results demonstrate that our constructions have acceptable efficiency in practice.

Bilateral Privacy-Preserving Worker Selection in Spatial Crowdsourcing

Spatial Crowdsourcing (SC) has been adopted in various applications such as Gigwalk and Uber, where a platform takes location-based tasks (e.g., picking up passengers) from a requester and selects suitable workers to perform them. In most existing works, the platform selects workers based on the requester and worker information, which suffers from serious privacy issues. Some works have considered privacy issues, but they still suffer from either of two limitations: (i) Privacy of the requester and worker cannot be protected simultaneously; (ii) Third-party trusted entities are usually required. Motivated by this, we focus on protecting the privacy of both the requester and the worker without third-party entities while selecting workers. We use randomized response, a widely recognized and prevalent privacy model achieving Local Differential Privacy (LDP), to jointly protect the privacy of workers’ locations and charges based on the location-charge correlation. For the requester, we present a novel mechanism called randomized matrix multiplication to hide the real task locations. More importantly, we prove that the worker selection based on the protected information is non-submodular and NP-hard, which cannot be addressed in polynomial time. To this end, we present an approximate algorithm to solve the problem efficiently, of which the effectiveness is measured by the approximation ratio, i.e., the ratio of the optimal solution to the approximate solution. Finally, simulations based on real-world datasets illustrate that our worker selection outperforms the state-of-the-art method on both privacy protection and worker selection.

How does brain-computer interface technology present challenges for labour law in New Zealand?

There is a disheartening narrative regarding the future of work found in some publications. It is that automation will replace workers in industrial and service sector unions relatively soon and artificial intelligence will replace knowledge workers in public sector unions such as teachers, nurses and public servants. Recent announcements of technological breakthroughs have challenged this disheartening narrative. Neuralink recently debuted polymer threads that could be inserted into brains allowing them to control certain computer functions. Rather than human workers being replaced by machines, brain-computer interfaces could allow them to have a symbiotic workplace relationship. Human workers could have access to the physical power of robots and the intellectual power of artificial general intelligence. However, while this brain-computer interface technology provides hope for those concerned about impending job loss it still poses significant legal challenges. This paper will focus on the three most pressing –the potential for discrimination against workers unable or unwilling to accept neural-prosthetics, the privacy of workers, and the prospect of compulsion by machines.

Health-Related Parameters for Evaluation Methodologies of Human Operators in Industry: A Systematic Literature Review

Human factors have always been an important part of research in industry, but more recently the idea of sustainable development has attracted considerable interest for manufacturing companies and management practitioners. Incorporating human factors into a decision system is a difficult challenge for manufacturing companies because the data related to human factors are difficult to sense and integrate into the decision-making processes. Our objectives with this review are to propose an overview of the different methods to measure human factors, of the solutions to reduce the occupational strain for workers and of the technical solutions to integrate these measures and solutions into a complex industrial decision system. The Scopus database was systematically searched for works from 2014 to 2021 that describe some aspects of human factors in industry. We categorized these works into three different classes, representing the specificity of the studied human factor. This review aims to show the main differences between the approaches of short-term fatigue, long-term physical strain and psychosocial risks. Long-term physical strain is the subject that concentrates the most research efforts, mainly with physical and simulation techniques to highlight physical constraints at work. Short-term fatigue and psychosocial constraints have become a growing concern in industry due to new technologies that increase the requirements of cognitive activities of workers. Human factors are taking an important place in the sustainable development of industry, in order to ameliorate working conditions. However, vigilance is required because health-related data creation and exploitation are sensible for the integrity and privacy of workers.

Crossed effects of audio-visual environment on indoor soundscape perception for pleasant open-plan office environments

An open-plan office (OPO) layout promotes communication and teamwork among workers and provides flexibility in working; however, it also adversely affects work satisfaction, health, and worker privacy, leading to dissatisfaction and poor task performance. In this study, the subjective responses in an audio-visual OPO environment were analyzed to investigate the effects of the environment on indoor soundscape perception for designing pleasant OPOs. The outdoor environments of four OPOs with four different visual layouts were divided based on two conditions with different window views (good and bad views) to model eight visual stimuli. The auditory aspect was evaluated by computer simulation using three sound sources. For subjective assessment, virtual reality technology was employed with 24 audio-visual stimuli, and thirty-four subjects were asked to rate the perceived affective quality and overall quality. Task performance was quantified using auditory digit span tasks. The spatial preference decreased with the Loud, Variable, and Reverberant attributes and increased with the Bright, Orderly, and Wide attributes. For the same OPO soundscape, the window area ratio and floor area showed positive correlations with visual privacy, visual satisfaction, speech privacy, and sound satisfaction. However, work satisfaction and task performance exhibited contradictory relationships with the visual parameters. Furthermore, wide, open workspaces yielded high work satisfaction, whereas narrow, confined work spaces yielded high task performance. These results indicate that the relationship between work satisfaction and task performance must be considered for a balanced OPO design, providing practical guidance for designing pleasant OPO environments.