scholarly journals Towards a Smart Privacy-Preserving Incentive Mechanism for Vehicular Crowd Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Lingling Wang ◽  
Zhongda Cao ◽  
Peng Zhou ◽  
Xueqin Zhao
Keyword(s):  
Privacy Preservation ◽  
Incentive Mechanism ◽  
Security Analysis ◽  
Privacy Preserving ◽  
Quality Data ◽  
High Quality Data ◽  
Execution Costs ◽  
Crowd Sensing ◽  
Traffic Data Collection

Vehicular crowd sensing is a promising approach to address the problem of traffic data collection by leveraging the power of vehicles. In various applications of vehicular crowd sensing, there exist two burning issues. First, privacy can be easily compromised when a vehicle is performing a crowd sensing task. Second, vehicles have no incentive to submit high-quality data due to the lack of fairness, which means that everyone gets the same paid, regardless of the quality of the submitted data. To address these issues, we propose a smart privacy-preserving incentive mechanism (SPPIM) for vehicular crowd sensing. Specifically, we first propose a new SPPIM model for the scenario of vehicular crowd sensing via smart contract on the blockchain. Then, we design a privacy-preserving incentive mechanism based on budget-limited reverse auction. Anonymous authentication based on zero-knowledge proof is utilized to ensure the privacy preservation of vehicles. To ensure fairness, the reward payments of winning vehicles are determined by not only the bids of vehicles but also their reputation and the data quality. Then, any rewarded vehicle can get the fair payment; on the contrary, malicious vehicles or task initiators will be punished. Finally, SPPIM is implemented by using smart contracts written via Solidity on a local Ethereum blockchain network. Both security analysis and experimental results show that the proposed SPPIM achieves privacy preservation and fair incentives at acceptable execution costs.

scholarly journals A Homomorphic Encryption Based Location Privacy Preservation Scheme for Crowdsensing Tasks Allocation

2021 ◽  
Author(s):  
Xiaodong Zheng ◽  
Qi Yuan ◽  
Bo Wang ◽  
Lei Zhang
Keyword(s):  
Experimental Verification ◽  
Task Allocation ◽  
Privacy Preservation ◽  
Location Privacy ◽  
Homomorphic Encryption ◽  
Security Analysis ◽  
Whole Process ◽  
Privacy Violation

Abstract In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

scholarly journals Identification of models of nonlinear dynamic processes in mining on the basis of Volterra nuclei

2020 ◽  
Vol 201 ◽  
pp. 01028
Author(s):  
Natalia Morkun ◽  
Iryna Zavsiehdashnia ◽  
Oleksandra Serdiuk ◽  
Iryna Kasatkina
Keyword(s):  
Volterra Series ◽  
Quality Data ◽  
Weight Functions ◽  
Mineral Concentration ◽  
High Quality Data ◽  
Technological Processes ◽  
Process Formation ◽  
Nonlinear Dynamic Processes ◽  
Controlled Object

Solving the problem of improving efficiency of technological processes of mineral concentration is one of the essential for providing sustainability of mining enterprises. Currently, special attention is paid to optimization of technological processes in concentration of useful minerals. This approach calls for availability of high-quality data on the process, formation of corresponding databases and their subsequent processing to build adequate and efficient mathematical models of processes and systems. In order to improve quality of mathematical description of forming fractional characteristics of ore through applying technological aggregates in concentration, the authors suggest using power Volterra series that provide characteristics of a controlled object (its condition) as a sequence of multidimensional weight functions invariant to the type of an input signal – Volterra nuclei. Application of Volterra structures enables decreasing the modelling error to 0.039 under the root-mean-square error of 0.0594.

scholarly journals A privacy-preserving collaborative reputation system for mobile crowdsensing

2018 ◽  
Vol 14 (9) ◽  
pp. 155014771880218 ◽  
Author(s):  
Bayan Hashr Alamri ◽  
Muhammad Mostafa Monowar ◽  
Suhair Alshehri
Keyword(s):  
Security Analysis ◽  
Privacy Preserving ◽  
Reputation System ◽  
Mobile Crowdsensing ◽  
Sensing Applications ◽  
Conflicting Objectives ◽  
Trusted Data ◽  
Privacy Issues ◽  
Open Nature

Mobile crowdsensing is an emerging technology in which participants contribute sensor readings for different sensing applications. This technology enables a broad range of sensing applications by utilizing smartphones and tablets worldwide to improve people’s quality of life. Protecting participants’ privacy and ensuring the trustworthiness of the sensor readings are conflicting objectives and key challenges in this field. Privacy issues arise from the disclosure of the participant-related context information, such as participants’ location. Trustworthiness issues arise from the open nature of sensing system because anyone can contribute data. This article proposes a privacy-preserving collaborative reputation system that preserves privacy and ensures data trustworthiness of the sensor readings for mobile crowdsensing applications. The proposed work also counters a number of possible attacks that might occur in mobile crowdsensing applications. We provide a detailed security analysis to prove the effectiveness of privacy-preserving collaborative reputation system against a number of attacks. We conduct an extensive simulation to investigate the performance of our schema. The obtained results show that the proposed schema is practical; it succeeds in identifying malicious users in most scenarios. In addition, it tolerates a large number of colluding adversaries even if their number surpass 65%. Moreover, it detects on-off attackers even if they report trusted data with high probability (0.8).

scholarly journals A mobile crowd sensing framework for suspect investigation: An objectivity analysis and de-identification approach

2020 ◽  
Vol 17 (1) ◽  
pp. 253-269
Author(s):  
Alaoui El ◽  
Fazziki El ◽  
Fatima Ennaji ◽  
Mohamed Sadgal
Keyword(s):  
Smart Cities ◽  
Poor Quality ◽  
Quality Data ◽  
Crowd Sensing ◽  
Private Data ◽  
Poor Quality Data ◽  
Identification Approach ◽  
Mobile Crowd ◽  
Share Information

The ubiquity of mobile devices and their advanced features have increased the use of crowdsourcing in many areas, such as the mobility in the smart cities. With the advent of high-quality sensors on smartphones, online communities can easily collect and share information. These information are of great importance for the institutions, which must analyze the facts by facilitating the data collecting on crimes and criminals, for example. This paper proposes an approach to develop a crowdsensing framework allowing a wider collaboration between the citizens and the authorities. In addition, this framework takes advantage of an objectivity analysis to ensure the participants? credibility and the information reliability, as law enforcement is often affected by unreliable and poor quality data. In addition, the proposed framework ensures the protection of users' private data through a de-identification process. Experimental results show that the proposed framework is an interesting tool to improve the quality of crowdsensing information in a government context.

scholarly journals Evaluation and optimization of ICOS atmosphere station data as part of the labeling process

2021 ◽  
Vol 14 (1) ◽  
pp. 89-116
Author(s):  
Camille Yver-Kwok ◽  
Carole Philippon ◽  
Peter Bergamaschi ◽  
Tobias Biermann ◽  
Francescopiero Calzolari ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Scientific Data ◽  
Quality Data ◽  
Observation System ◽  
Quality Level ◽  
High Quality Data ◽  
Site Water ◽  
Quantitative Results ◽  
Greenhouse Gas Budget

Abstract. The Integrated Carbon Observation System (ICOS) is a pan-European research infrastructure which provides harmonized and high-precision scientific data on the carbon cycle and the greenhouse gas budget. All stations have to undergo a rigorous assessment before being labeled, i.e., receiving approval to join the network. In this paper, we present the labeling process for the ICOS atmosphere network through the 23 stations that were labeled between November 2017 and November 2019. We describe the labeling steps, as well as the quality controls, used to verify that the ICOS data (CO2, CH4, CO and meteorological measurements) attain the expected quality level defined within ICOS. To ensure the quality of the greenhouse gas data, three to four calibration gases and two target gases are measured: one target two to three times a day, the other gases twice a month. The data are verified on a weekly basis, and tests on the station sampling lines are performed twice a year. From these high-quality data, we conclude that regular calibrations of the CO2, CH4 and CO analyzers used here (twice a month) are important in particular for carbon monoxide (CO) due to the analyzer's variability and that reducing the number of calibration injections (from four to three) in a calibration sequence is possible, saving gas and extending the calibration gas lifespan. We also show that currently, the on-site water vapor correction test does not deliver quantitative results possibly due to environmental factors. Thus the use of a drying system is strongly recommended. Finally, the mandatory regular intake line tests are shown to be useful in detecting artifacts and leaks, as shown here via three different examples at the stations.

scholarly journals Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4486 ◽  
Author(s):  
Mohan Li ◽  
Yanbin Sun ◽  
Yu Jiang ◽  
Zhihong Tian
Keyword(s):  
Low Cost ◽  
Real Life ◽  
Quality Data ◽  
Parameter Setting ◽  
Multiple Sources ◽  
High Quality ◽  
High Quality Data ◽  
Real Life Data ◽  
Sensor Cloud

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.

scholarly journals A Data Quality Control Program for Computer-Assisted Personal Interviews

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Janet E. Squires ◽  
Alison M. Hutchinson ◽  
Anne-Marie Bostrom ◽  
Kelly Deis ◽  
Peter G. Norton ◽  
...  
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Control Program ◽  
Quality Data ◽  
Computer Assisted ◽  
Data Quality Control ◽  
Process Data ◽  
Quality Control Program ◽  
High Quality Data

Researchers strive to optimize data quality in order to ensure that study findings are valid and reliable. In this paper, we describe a data quality control program designed to maximize quality of survey data collected using computer-assisted personal interviews. The quality control program comprised three phases: (1) software development, (2) an interviewer quality control protocol, and (3) a data cleaning and processing protocol. To illustrate the value of the program, we assess its use in the Translating Research in Elder Care Study. We utilize data collected annually for two years from computer-assisted personal interviews with 3004 healthcare aides. Data quality was assessed using both survey and process data. Missing data and data errors were minimal. Mean and median values and standard deviations were within acceptable limits. Process data indicated that in only 3.4% and 4.0% of cases was the interviewer unable to conduct interviews in accordance with the details of the program. Interviewers’ perceptions of interview quality also significantly improved between Years 1 and 2. While this data quality control program was demanding in terms of time and resources, we found that the benefits clearly outweighed the effort required to achieve high-quality data.

scholarly journals A re-analysis of the 58 year mass-balance record of Storglaciären, Sweden

2005 ◽  
Vol 42 ◽  
pp. 389-394 ◽  
Author(s):  
Per Holmlund ◽  
Peter Jansson ◽  
Rickard Pettersson
Keyword(s):  
Time Series ◽  
Mass Balance ◽  
Measurement Methods ◽  
Summer Temperature ◽  
Quality Data ◽  
Glacier Mass Balance ◽  
Equilibrium Line Altitude ◽  
High Quality Data ◽  
Time Periods

AbstractThe use of glacier mass-balance records to assess the effects of glacier volume change from climate change requires high-quality data. The methods for measuring glacier mass balance have been developed in tandem with the measurements themselves, which implies that the quality of the data may change with time. We have investigated such effects on the mass-balance record of Storglaciären, Sweden, by re-analyzing the records using a better map base and applying successive maps over appropriate time periods. Our results show that errors <0.8 m occur during the first decades of the time series. Errors decrease with time, which is consistent with improvements in measurement methods. Comparison between the old and new datasets also shows improvements in the relationships between net balance, equilibrium-line altitude and summer temperature. A time-series analysis also indicates that the record does not contain longer-term (>10 year) oscillations. The pseudo-cyclic signal must thus be explained by factors other than cyclically occurring phenomena, although the record may still be too short to establish significant signals. We strongly recommend re-analysis of long mass-balance records in order to improve the mass-balance records used for other analyses.

scholarly journals The data and analysis underlying NIH’s decision to cap research support lacked rigor and transparency: a commentary

2017 ◽  
Author(s):  
A Cecile JW Janssens ◽  
Gary W Miller ◽  
K Venkat Narayan
Keyword(s):  
Scientific Output ◽  
National Institutes Of Health ◽  
Quality Data ◽  
Rigorous Analysis ◽  
Research Support ◽  
High Quality Data ◽  
Grant Support ◽  
Threshold Data ◽  
The Us

The US National Institutes of Health (NIH) recently announced that they would limit the number of grants per scientist and redistribute their funds across a larger group of researchers. The policy was withdrawn a month later after criticism from the scientific community. Even so, the basis of this defunct policy was flawed and it merits further examination. The amount of grant support would have been quantified using a new metric, the Grant Support Index (GSI), and limited to a maximum of 21 points, the equivalent of three R01 grants. This threshold was decided based upon analysis of a new metric of scientific output, the annual weighted Relative Citation Ratio, which showed a pattern of diminishing returns at higher values of the GSI. In this commentary, we discuss several concerns about the validity of the two metrics and the quality of the data that the NIH had used to set the grant threshold. These concerns would have warranted a re-analysis of new data to confirm the legitimacy of the GSI threshold. Data-driven policies that affect the careers of scientists should be justified by nothing less than a rigorous analysis of high-quality data.

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