Excessive Data Collection Research Articles

Excessive Data Collection as Anti-Competitive Conduct: The Approach of Germany and India

Excessive Data Collection as Anti-Competitive Conduct: The Approach of Germany and India

Verifiable privacy-preserving association rule mining using distributed decryption mechanism on the cloud

As one of the important ways in data mining, the association rule mining is to analyze the correlation of transactions based on massive data and mine the hidden valuable information. However, excessive data collection and analysis might lead to the privacy leakage of user data and the damage of data integrity. Meanwhile, in the existing privacy-preserving schemes, the cloud servers such as the evaluator have strong decryption capabilities, which causes active attacks easily, or data reliability in mining and analysis is not considered. In response to these problems, this paper proposes a verifiable privacy-preserving association rule mining scheme (VPPARM) using distributed decryption mechanism on the cloud. First, the scheme adopts distributed decryption to complete the data mining tasks through the dual-cloud servers, which weakens the decryption ability of the cloud servers and prevents the server from active attacks. Secondly, to protect the privacy of the association rule mining process, our scheme adopts adding virtual transactions, permutations, and random number masking collaboratively to hide the data in the whole mining process. In addition, to eliminate the hidden danger of illegal users, our scheme adopts a verifiable short digital signature scheme to verify data integrity and ensure data reliability, avoiding the poisoning attack. Finally, through the performance evaluation, the results demonstrate that our scheme realizes correctness, security, reliability with lower communication and computation costs and improves efficiency to a certain extent.

Excessive Data Collection as an Abuse of Dominant Position. The Implications of the Digital Data Era on EU Competition Law and Policy

Excessive Data Collection as an Abuse of Dominant Position. The Implications of the Digital Data Era on EU Competition Law and Policy

Hidden depths: The effects of extrinsic data collection on consumer insurance contracts

Commentators have predicted that the insurance industry will soon benefit from technological advancements, such as developments in Artificial Intelligence (‘AI’) and Big Data. The application of AI- and Big Data-powered tools promises cost reduction, the creation of innovative products, and the potential to offer more efficient and tailored services to consumers. However, these new opportunities are mirrored by new legal and regulatory challenges. This article discusses challenges facing Australian data protection law, focusing on (potential) collection of consumers' data by insurers from non-traditional sources. In particular, we examine situations in which consumers may not be aware that the data collected could end up being used to price insurance. In our analysis, we discuss two useful examples of such non-traditional data sources: customer loyalty schemes and social media. These may give rise to several concerning data practices, including a significant increase in the collection of consumers' data by insurers. We argue that datafication of insurer processes may fuel excessive data collection in the context of insurance contracts, generating a substantial risk of harm to consumers, especially in terms of discrimination, exclusion, and unaffordability of insurance. We complement our analysis with the discussion of Australian insurance-specific provisions, asking if, and how, the harms examined could be adequately addressed.

What Drives Authorization in Mobile Applications? A Perspective of Privacy Boundary Management

Personal information has been likened to “golden data”, which companies have chased using every means possible. Via mobile apps, the incidents of compulsory authorization and excessive data collection have evoked privacy concerns and strong repercussions among app users. This manuscript proposes a privacy boundary management model, which elaborates how such users can demarcate and regulate their privacy boundaries. The survey data came from 453 users who authorized certain operations through mobile apps. The partial least squares (PLS) analysis method was used to validate the instrument and the proposed model. Results indicate that information relevance and transparency play a significant role in shaping app users’ control–risk perceptions, while government regulation is more effective than industry self-discipline in promoting the formation of privacy boundaries. Unsurprisingly, privacy risk control perceptions significantly affect users’ privacy concerns and trust beliefs, which are two vital factors that ultimately influence their willingness to authorize. The implications of conducting a thorough inquiry into app users’ willingness to authorize their privacy information are far-reaching. In relation to this, app vendors should probe into the privacy-relevant beliefs of their users and enact effective privacy practices to intercept the economic and reputational damages induced by improper information collection. More significantly, a comprehensive understanding of users’ willingness to authorize their information can serve as an essential reference for relevant regulatory bodies to formulate reasonable privacy protection policies in the future.

Excessive Data Collection as a Form of Anticompetitive Conduct: The German Facebook Case

In a high-profile decision of February 6, 2019, the German Federal Cartel Office prohibited Facebook’s data collection policy as an abuse of dominance for infringing its users’ constitutional right to privacy. The case triggered a remarkable interinstitutional dispute between the key players in German competition law. Conflicting rulings by the Düsseldorf Higher Regional Court and the German Federal Court of Justice further illustrate how deeply divided the antitrust community is on the role of competition law in regulating excessive data collection and other novel types of harm caused by dominant digital platforms. This contribution discusses the original prohibition decision, the ensuing court orders, and legislative reform proposals in the broader context of European Union and U.S. competition law.

Excessive data collection: Privacy considerations and abuse of dominance in the era of big data

The present era of big data challenges many of the basic assumptions of competition law. One particularly pressing question is: to what extent should privacy considerations and data protection filter into competition law assessments? The paper focuses on personalized user data that companies gather through third-party tracking on the web. This practice allows the tracker to build comprehensive user profiles across platforms and devices, placing an unprecedented volume of up-to-date personal data in the hands of a small number of globally active companies. Focusing on the stage of data acquisition, the paper assesses two scenarios under which EU competition law may deem the vast amounts of data gathered by certain digital platforms excessive under Article 102(a) TFEU: excessive data “prices” and data policies as unfair trading conditions. In both cases, the competition law assessment is autonomous from other areas of the law: while a breach of data protection rules is not automatically a breach of competition law, a company adhering to data protection rules may still violate competition laws. The paper finds that EU competition law already possesses the necessary tools to address excessive data collection, while data protection rules provide much-needed context for this type of exploitative abuse.

Excessive Data Collection as a Form of Anticompetitive Conduct – The German Facebook Case

In a high-profile decision of 6 February 2019, the German Federal Cartel Office prohibited Facebook’s data collection policy as an abuse of dominance for infringing its users’ constitutional right to privacy. The case triggered a remarkable inter-institutional dispute between the key players in German competition law. Conflicting rulings by the Dusseldorf Higher Regional Court and the German Federal Court of Justice further illustrate how deeply divided the antitrust community is on the role of competition law in regulating excessive data collection and other novel types of harm caused by dominant digital platforms. This contribution discusses the original prohibition decision, the ensuing courts orders and legislative reform proposals in the broader context of EU and US competition law.

The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy

Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.

The Dilemma of Data’s Future Value: User Perceptions of the Sharing Economy

Through excessive data collection and its subsequent analysis, sharing economy companies, such as Uber and Airbnb, are able to generate immense additional value. This has made extracting user data ...

Excessive Data Collection: Privacy Considerations and Abuse of Dominance in the Era of Big Data

The digital economy challenges many of the basic assumptions of competition law. With the rise of big data and big data analytics, one question has become particularly pressing: to what extent should privacy considerations and data protection laws filter into competition law assessments? The present paper focuses on personalized user data that companies gather online through third-party tracking. This practice allows the tracker to build comprehensive user profiles across platforms and across devices, resting an unprecedented volume of up-to-date personal data in the hands of a small number of globally active companies. Starting from the question whether and to what extent competition law should be concerned about privacy, the paper addresses the question whether the vast amounts of data gathered by certain companies through third-party tracking may be deemed excessive under Article 102(a) TFEU, thus making an analogy with excessive prices possible. It is also considered whether excessive data collection may constitute a stand-alone abuse under the heading of unfair trading conditions, based on a decrease in quality or the mere excessiveness of the personal data gathered. In this context, the paper discusses the recent German Facebook decision as a test case for excessive data collection.

Facebook’s Anticompetitive Lean in Strategies

Facebook is under fire on several fronts and with good reason. Regulators strive to make sense of and address a plethora of seemingly unrelated issues that arise from the operation of its platform. These range from antitrust, privacy violations, dissemination of harmful content and speech, deception and polarisation to political manipulation. This paper identifies Facebook’s unrestricted and excessive data collection as a unifying theme that requires immediate antitrust action. Once a privacy-oriented social network, Facebook soon mutated into a surveillance machine designed to hoover people’s personal data to identify and understand people’s interests, preferences and emotions and turn that knowledge into profit through the sale of targeted ads. Since people’s innate preference for privacy stood in the way of Facebook’s growth, Facebook resorted to privacy intrusions and deception to access as much user data as possible, thereby gaining market power. Currently, its overwhelming dominant position in the social media market means that no matter how much data Facebook extracts from users, how transparent its information about its data processing practices is and how many privacy scandals ensue from its reckless handling of data, users have nowhere else to go. This paper provides a course of action to correct this unacceptable anticompetitive outcome. The imposition of unfair commercial terms on consumers, the distortion of the competitive process through privacy violations and misleading practices, the squeezing of news publishers’ traffic and foreclosure of actual and potential competitors by Facebook, can be stopped. A combination of data and consumer protection measures alone cannot stop Facebook’s actions, but antitrust enforcement can be used to curb Facebook’s ability to reinforce its data-driven abuse of its market power.

Streamlining Safety Data Collection in Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia Trials: Recommendations of the Clinical Trials Transformation Initiative Antibacterial Drug Development Project Team.

Resistant bacteria are one of the leading causes of hospital-acquired/ventilator-associated bacterial pneumonia (HABP/VABP). HABP/VABP trials are complex and difficult to conduct due to the large number of medical procedures, adverse events, and concomitant medications involved. Differences in the legislative frameworks between different regions of the world may also lead to excessive data collection. The Clinical Trials Transformation Initiative (CTTI) seeks to advance antibacterial drug development (ABDD) by streamlining clinical trials to improve efficiency and feasibility while maintaining ethical rigor, patient safety, information value, and scientific validity. In 2013, CTTI engaged a multidisciplinary group of experts to discuss challenges impeding the conduct of HABP/VABP trials. Separate workstreams identified challenges associated with current data collection processes. Experts defined "data collection" as the act of capturing and reporting certain data on the case report form as opposed to recording of data as part of routine clinical care. The ABDD Project Team developed strategies for streamlining safety data collection in HABP/VABP trials using a Quality by Design approach. Current safety data collection processes in HABP/VABP trials often include extraneous information. More targeted strategies for safety data collection in HABP/VABP trials will rely on optimal protocol design and prespecification of which safety data are essential to satisfy regulatory reporting requirements. A consensus and a cultural change in clinical trial design and conduct, which involve recognition of the need for more efficient data collection, are urgently needed to advance ABDD and to improve HABP/VABP trials in particular.

Application of Six Sigma to Achieve Sales Lifecycle Optimization

The objective of this paper is to optimize sales lifecycle performance at United Biosource Corporation through the Six Sigma DMAIC methodology to produce fact based, and data driven approaches with reduced process inconsistency. This methodology was applied to identify inefficiencies within the current sales lifecycle process, including excessive data collection and workflow activities. Evaluations of these inefficiencies identified critical to quality and critical to process inputs. On average, 285.33 hours per month are lost as a result of Smartweb related data and workflow performance activities. The identified drivers of these activities are: (i) external workflows; (ii) data cleaning; and (iii) outside reporting. The goal is to decrease the total number of hours per month spent performing these activities by 75% by the end of 2014. These results will be included in a subsequent publication. To date, the application of the Six Sigma DMAIC methodology to optimize sales lifecycle helped identify inefficiencies, develop solutions and a control plan to ensure the improvements are sustainable.

Utility of an Electronic Monitoring and Reminder System for Enhancing Hand Hygiene Practices in a Pediatric Oncology Unit

Project: A gap analysis was performed as the initial step in the infection prevention program (IPP) redesign. This was performed by a team of stakeholders with strong leadership support. This analysis evaluated elements that were considered to be barriers to an effective and efficient IPP. The elements were placed into 4 main categories; resources; process; service; and regulatory compliance. Technology lag and limited IP resources were identified in addition to the limited IT support. The process analysis revealed that it is highly manual, retrospective, minimal analysis, with excessive data collection, with limited focus on prevention. The service analysis identified the limited visibility, collaboration, feedback, and that hospital staff was not aware of the IPP role and value. Finally, analysis of the regulatory compliance identified the increasing demands on the IPP. Results: ThegapanalysisresultedinrecommendationstoenhancetheIPPbyaddressingtheidentifiedbarriers.The cornerstone of these recommendations was to purchase a commercially available ESS to provide system risk-based surveillance. This was also accompanied by identifying the need to increase the staffing of the department. The realtime data availability and the ability to receive electronic alerts allowed IP staff to initiate daily patient care unit roundingwhichinturnincreasedtheirvisibility,butmoreimportantlycreatedadditionalHAIpreventionopportunities. Lessons Learned: A thorough gap analysis is essential in establishing infection prevention program priorities and gaining organizational support. The needs assessment and gap analysis should include as many stakeholders as possible, but leadership involvement is a must. Furthermore, role shift is paramount to the process and the shift from data collection into using data to initiate intervention which is the key to the success of our program.