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WIREs Data Mining and Knowledge Discovery, 2014 Data privacy studies methods, tools, and theory to avoid the disclosure of sensitive information. Its origin is in statistics with the goal to ensure the confidentiality of data gathered from census and questionnaires. The topic was latter introduced in computer science and more particularly in data mining, where due to the large amount of data ...
Vicenç Torra, Guillermo Navarro-Arribas
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Privacy of Outsourced Data [PDF]
, 2010 Data outsourced to an external storage server are usually encrypted since there is the common assumption that all data are equally sensitive. The encrypted data however cannot be efficiently queried and their selective release is not possible or require the application of specific solutions.
S. De Capitani di Vimercati, S. Foresti
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Statistical Data Privacy: A Song of Privacy and Utility
Annual Review of Statistics and Its Application, 2023 To quantify trade-offs between increasing demand for open data sharing and concerns about sensitive information disclosure, statistical data privacy (SDP) methodology analyzes data release mechanisms that sanitize outputs based on confidential data. Two dominant frameworks exist: statistical disclosure control (SDC) and the more recent differential ...
Aleksandra B. Slavkovic, Jeremy Seeman
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Privacy in Data Service Composition [PDF]
IEEE Transactions on Services Computing, 2020 In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have different privacy policies. Answering many end-users' legitimate queries requires the integration of data from multiple such services.
Barhamgi, Mahmoud +5 more
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, 2004 There is increasing need to build information systems that protect the privacy and ownership of data without impeding the flow of information. We will present some of our current work to demonstrate the technical feasibility of building such systems.
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Privacy Goals for the Data Lifecycle
Future Internet, 2022 The introduction of Data Protection by Default and Design (DPbDD) brought in as part of the General Data Protection Regulation (GDPR) in 2018, has necessitated that businesses review how best to incorporate privacy into their processes in a transparent manner, so as to build trust and improve decisions around privacy best practice.
Jane Henriksen-Bulmer +3 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Sickle cell disease (SCD) is a chronic, inherited hemoglobinopathy that requires frequent hospitalization for disease‐related complications. Canadian data on inpatient care is limited. This study compared caregiver‐reported hospital experiences of children with SCD to those with cystic fibrosis (CF), a chronic, autosomal recessive ...
Hailey M. Zwicker +11 more
wiley +1 more source
Privacy Preserving Data Mining [PDF]
Journal of Cryptology, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yehuda Lindell, Benny Pinkas
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Wilms tumor (WT) treatment imposes a significant time burden on patients and their families. Time toxicity is a patient‐centered metric that quantifies the burden of healthcare interaction. We sought to define time toxicity in the first year after diagnosis of WT and hypothesized that it would increase as tumor stage and treatment ...
Caleb Q. Ashbrook +6 more
wiley +1 more source
Privacy-Preserving Data Mashup [PDF]
2011 IEEE International Conference on Advanced Information Networking and Applications, 2011 Data Mashup is a special class of mashup application that combines information on the fly from multiple data sources to respond to transient business needs. Mashing up data requires an important programming skill on the side of mashups' creators, and involves handling many challenging privacy and security concerns raised by data providers.
Barhamgi, Mahmoud +3 more
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