Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Manish Kesarwani"'
Publikováno v:
Data Science and Engineering, Vol 3, Iss 4, Pp 323-340 (2018)
Abstract Prior solutions for securely handling SQL range predicates in outsourced Cloud-resident databases have primarily focused on passive attacks in the Honest-but-Curious adversarial model, where the server is only permitted to observe the encryp
Externí odkaz:
https://doaj.org/article/6ef209502f4f47b1bd76e693393c67b0
Autor:
Sanjit Chatterjee, Jayam Modi, Akash Shah, Manish Kesarwani, Sayantan Mukherjee, Shravan Kumar Parshuram Puria
Publikováno v:
International Journal of Information Security. 20:199-244
In this work, we investigate the problem of secure wildcard search over encrypted data. The setting comprises of three entities, viz. the data owner, the server and the client. The data owner outsources the encrypted data to the server, who oblivious
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9455898a9e5652fdea994de241caacb0
https://doi.org/10.1007/s10207-020-00492-w
https://doi.org/10.1007/s10207-020-00492-w
Publikováno v:
CLOUD
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on third parti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7b2276955c4bc7386cec60be8eb82861
http://arxiv.org/abs/2108.04780
http://arxiv.org/abs/2108.04780
Publikováno v:
CLOUD
Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can lead to data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0cd36b5add8591a1625e91acfe80a4ac
Publikováno v:
SMDS
Data exploration and quality analysis is an important yet tedious process in the AI pipeline. Current data cleaning and data readiness assessment practices for machine learning tasks are mostly conducted in an arbitrary manner which limits their reus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e0bbe496b94304979b92b1967c657e32
http://arxiv.org/abs/2010.07213
http://arxiv.org/abs/2010.07213
Publikováno v:
ACSAC
Cloud vendors are increasingly offering machine learning services as part of their platform and services portfolios. These services enable the deployment of machine learning models on the cloud that are offered on a pay-per-query basis to application
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31f1871883965720463f9cae5e2fba01
http://arxiv.org/abs/1711.07221
http://arxiv.org/abs/1711.07221