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Assuring privacy and reliability in crowdsourcing with coding

Lav R. Varshney, Aditya Vempaty, Pramod K. Varshney
2014 2014 Information Theory and Applications Workshop (ITA)  
Hence there is a need to ensure reliable work delivery while preserving some level of privacy to the requester's data.  ...  We develop mathematical models to study the precise tradeoffs between task performance quality, level of privacy against collusion attacks, and cost of invoking a large crowd.  ...  For example, a labeling task might be to transform the raw text of an email into a label drawn from a finite set like amusing, serious, irrelevant.  ... 
doi:10.1109/ita.2014.6804213 dblp:conf/ita/VarshneyVV14 fatcat:rjdyjgyvjrhz3joks5o63bwi4e

Key Research Issues and Related Technologies in Crowdsourcing Data Collection

Yunhui Li, Liang Chang, Long Li, Xuguang Bao, Tianlong Gu, Lihua Yin
2021 Wireless Communications and Mobile Computing  
In recent years, a large amount of effort has been spent on crowdsourcing in data collection, to address the challenges, including quality control, cost control, efficiency, and privacy protection.  ...  In this paper, we introduce the concept and workflow of crowdsourcing data collection.  ...  Privacy-Preserving. Privacy is an important issue in crowdsourcing.  ... 
doi:10.1155/2021/8745897 fatcat:vobajfz7i5bnnpfvx6vo2kzmxa

Human-Imitating Metrics for Training and Evaluating Privacy Preserving Emotion Recognition Models Using Sociolinguistic Knowledge [article]

Mimansa Jaiswal, Emily Mower Provost
2021 arXiv   pre-print
Privacy preservation is a crucial component of any real-world application.  ...  We show how certain commonly-used methods that seek to preserve privacy do not align with human perception of privacy preservation leading to distrust about model's claims.  ...  Further, we assess how the inclusion of privacy preservation in model training can lead to spurious correlations between text based features of the input and the target label, such as emotion.  ... 
arXiv:2104.08792v2 fatcat:irogim2gbbhkzcvhqt643jgv4e

Analyzing Privacy Policies Using Contextual Integrity Annotations [article]

Yan Shvartzshnaider, Noah Apthorpe, Nick Feamster, Helen Nissenbaum
2018 arXiv   pre-print
We then demonstrate that crowdsourcing can effectively produce CI annotations of privacy policies at scale.  ...  The resulting high precision annotations indicate that crowdsourcing could be used to produce a large corpus of annotated privacy policies for future research.  ...  preserving efforts in technical fields.  ... 
arXiv:1809.02236v1 fatcat:mqmbkljujjgo7lh3bszmo4ffri

Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing [article]

Boaz Shmueli, Jan Fell, Soumya Ray, Lun-Wei Ku
2021 arXiv   pre-print
We draw attention to the lack of ethical considerations related to the various tasks performed by workers, including labeling, evaluation, and production.  ...  We find that the Final Rule, the common ethical framework used by researchers, did not anticipate the use of online crowdsourcing platforms for data collection, resulting in gaps between the spirit and  ...  all crowdsourced-enabled research to go through an IRB application.  ... 
arXiv:2104.10097v1 fatcat:qjlcivrkrfhmffyauvnxipmcfy

TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations [article]

Ang Li, Yixiao Duan, Huanrui Yang, Yiran Chen, Jianlei Yang
2020 arXiv   pre-print
The emerging privacy concerns from users on data sharing hinder the generation or use of crowdsourcing datasets and lead to hunger of training data for new deep learning applications.  ...  To tackle the case where the learning task may be unknown or changing, we present TIPRDC, a task-independent privacy-respecting data crowdsourcing framework with anonymized intermediate representation.  ...  Visual Privacy Protection: Some works have been done to specifically preserve privacy in images and videos.  ... 
arXiv:2005.11480v6 fatcat:jgppdb5fanby5ocdjrjkvpeoeq

Privacy-preserving Active Learning on Sensitive Data for User Intent Classification [article]

Oluwaseyi Feyisetan, Thomas Drake, Borja Balle, Tom Diethe
2019 arXiv   pre-print
In this paper, we describe an approach for carrying out privacy preserving active learning with quantifiable guarantees.  ...  However, it requires sending data to annotators for labeling. This presents a possible privacy leak when the training set includes sensitive user data.  ...  Privacy-preserving machine learning k-Anonymity At first glance, a straightforward approach for addressing the privacy concerns of active learning could be through k-anonymity (Sweeney 2002; Di Castro  ... 
arXiv:1903.11112v1 fatcat:a6fssjx4a5aq5giif4g44qh4mu

A Review of Data Cleaning Methods for Web Information System

Jinlin Wang, Xing Wang, Yuchen Yang, Hongli Zhang, Binxing Fang
2019 Computers Materials & Continua  
, and privacy preservation.  ...  Then, after elaborating and analyzing each category, we summarize the descriptions and challenges of data cleaning methods with sub-elements such as data & user interaction, data quality rule, model, crowdsourcing  ...  The active learning method, as the optimization strategy for the crowdsourcing database labeling task, can reduce the problem scale through package and allows crowdsourcing application to label bigger  ... 
doi:10.32604/cmc.2020.08675 fatcat:jusi6zu7rzg65po5sowrpxlwxm

Big Data Analytics for Cyber Security

Pelin Angin, Bharat Bhargava, Rohit Ranchal
2019 Security and Communication Networks  
Crowdsourcing can be an effective way to quickly obtain a large labeled dataset at low cost, but the crowd annotations may be of lower quality than those of experts.  ...  Automated data mining can help in extracting important information from unstructured text for various cybersecurity use cases.  ... 
doi:10.1155/2019/4109836 fatcat:67lzxtvobjdendowq4dehvgp2y

Geotagging: Systematic Anatomization and Conceptual Model for POI Verification

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
and a point to point privacy-preserving data aggregation methods were proposed [45] .  ...  Threefold architecture for mobile crowdsourcing, mobile sensing, eHealth, smart grid, additive homomorphic encryption scheme, Sum and Min aggregate of time-series data, a privacy-preserving sum aggregation  ... 
doi:10.35940/ijitee.k7820.0991120 fatcat:twxah67m2vdldaweyqtrgvnh4a

Scaling requirements extraction to the crowd: Experiments with privacy policies

Travis D. Breaux, Florian Schaub
2014 2014 IEEE 22nd International Requirements Engineering Conference (RE)  
The task consists of extracting descriptions of data collection, sharing and usage requirements from privacy policies.  ...  To begin to address this issue, we conducted three experiments to evaluate crowdsourcing a manual requirements extraction task to a larger number of untrained workers.  ...  In addition, we manually split paragraphs to yield text spans less than 120 words, noting that we preserved some large spans when anaphora referred back to a previous sentence (e.g., when "This information  ... 
doi:10.1109/re.2014.6912258 dblp:conf/re/BreauxS14 fatcat:j2h7l3bz2fbajmopapgqfsmirm

AUTOREB

Deguang Kong, Lei Cen, Hongxia Jin
2015 Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security - CCS '15  
Moreover, it uses a crowdsourcing approach to automatically aggregate the security issues from review-level to app-level.  ...  Along with the increasing popularity of mobile devices, there exist severe security and privacy concerns for mobile apps.  ...  Table 1 : Security/Privacy-related behaviors including information retrieval, text mining, and machine learning.  ... 
doi:10.1145/2810103.2813689 dblp:conf/ccs/KongCJ15 fatcat:5matrajrovax5gpoavrgszcqja

When crowdsourcing meets mobile sensing: a social network perspective

Pin-Yu Chen, Shin-Ming Cheng, Pai-Shun Ting, Chia-Wei Lien, Fu-Jen Chu
2015 IEEE Communications Magazine  
This article investigates the structure of mobile sensing schemes and introduces crowdsourcing methods for mobile sensing.  ...  Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.  ...  Text relevance judgment The text relevance judgment dataset is provided by the text retrieval conference (TREC) crowdsourcing track in 2011 2 , 2 https://sites.google.com/site/treccrowd/2011 where 689  ... 
doi:10.1109/mcom.2015.7295478 fatcat:si7irugt5zdu3f7yrt4h4e5xtq

Federated Learning Meets Natural Language Processing: A Survey [article]

Ming Liu, Stella Ho, Mengqi Wang, Longxiang Gao, Yuan Jin, He Zhang
2021 arXiv   pre-print
Since text data is widely originated from end users, in this work, we look into recent NLP models and techniques which use federated learning as the learning framework.  ...  Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy.  ...  In Federated LMs, the commonly used privacy preserving technique is differential privacy (DP) [9] .  ... 
arXiv:2107.12603v1 fatcat:ebi4i6jnxbhihe7zuqx4uposbm

Looking at Cities in Mexico with Crowds

Darshan Santani, Salvador Ruiz-Correa, Daniel Gatica-Perez
2015 Proceedings of the 2015 Annual Symposium on Computing for Development - DEV '15  
Through the use of collective action, participatory sensing and mobile crowdsourcing, our study engages citizens to understand socio-urban problems in their communities.  ...  Finally, we investigate whether the perceptions of urban environments vary across different times of the day and found that places in the evening are perceived as less happy, pleasant and preserved, when  ...  We focus our pair-wise analysis on two statistically significant labels (happy and preserved), in addition to examining the non-significant dangerous label. Figure 7 shows the respective plots.  ... 
doi:10.1145/2830629.2830638 dblp:conf/dev/SantaniRG15 fatcat:pkyug4mc5ncpza232ad2bbvyre
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