Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Vatsalan, Dinusha"'
In the absence of data protection measures, software applications lead to privacy breaches, posing threats to end-users and software organisations. Privacy Enhancing Technologies (PETs) are technical measures that protect personal data, thus minimisi
Externí odkaz:
http://arxiv.org/abs/2401.00879
Several applications require counting the number of distinct items in the data, which is known as the cardinality counting problem. Example applications include health applications such as rare disease patients counting for adequate awareness and fun
Externí odkaz:
http://arxiv.org/abs/2301.04000
Publikováno v:
Springer Encyclopedia of Big Data Technologies, 2022
Given several databases containing person-specific data held by different organizations, Privacy-Preserving Record Linkage (PPRL) aims to identify and link records that correspond to the same entity/individual across different databases based on the
Externí odkaz:
http://arxiv.org/abs/2212.05682
Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privacy and confidentiality concerns
Externí odkaz:
http://arxiv.org/abs/2211.02161
Privacy-preserving estimation of counts of items in streaming data finds applications in several real-world scenarios including word auto-correction and traffic management applications. Recent works of RAPPOR and Apple's count-mean sketch (CMS) algor
Externí odkaz:
http://arxiv.org/abs/2208.05264
Record linkage algorithms match and link records from different databases that refer to the same real-world entity based on direct and/or quasi-identifiers, such as name, address, age, and gender, available in the records. Since these identifiers gen
Externí odkaz:
http://arxiv.org/abs/2206.15089
Autor:
Masood, Rahat, Cheng, Wing Yan, Vatsalan, Dinusha, Mishra, Deepak, Asghar, Hassan Jameel, Kaafar, Mohamed Ali
Sensors embedded in mobile smart devices can monitor users' activity with high accuracy to provide a variety of services to end-users ranging from precise geolocation, health monitoring, and handwritten word recognition. However, this involves the ri
Externí odkaz:
http://arxiv.org/abs/2205.06641
Autor:
Tonni, Shakila Mahjabin, Vatsalan, Dinusha, Farokhi, Farhad, Kaafar, Dali, Lu, Zhigang, Tangari, Gioacchino
Machine learning (ML) models have been shown to be vulnerable to Membership Inference Attacks (MIA), which infer the membership of a given data point in the target dataset by observing the prediction output of the ML model. While the key factors for
Externí odkaz:
http://arxiv.org/abs/2002.06856
Privacy-Preserving Record Linkage (PPRL) supports the integration of sensitive information from multiple datasets, in particular the privacy-preserving matching of records referring to the same entity. PPRL has gained much attention in many applicati
Externí odkaz:
http://arxiv.org/abs/1911.12930
Privacy-preserving record linkage (PPRL) aims at integrating sensitive information from multiple disparate databases of different organizations. PPRL approaches are increasingly required in real-world application areas such as healthcare, national se
Externí odkaz:
http://arxiv.org/abs/1701.01232