Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Johes Bater"'
Publikováno v:
Proceedings of the 2022 International Conference on Management of Data.
In this paper, we consider secure outsourced growing databases that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view, such that they can use only the materialized view to process qu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b168602359fb8223aae4d556bfdbd907
https://doi.org/10.1145/3514221.3526151
https://doi.org/10.1145/3514221.3526151
Publikováno v:
Proceedings of the VLDB Endowment. 13:2691-2705
A private data federation enables clients to query the union of data from multiple data providers without revealing any extra private information to the client or any other data providers. Unfortunately, this strong end-to-end privacy guarantee requi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0d4f5c4f762e1a81f64bff589dbaf89
https://doi.org/10.14778/3407790.3407854
https://doi.org/10.14778/3407790.3407854
Organizations often collect private data and release aggregate statistics for the public's benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals desc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27787222b5bba9dbb0fc131e63140fad
http://arxiv.org/abs/2201.05964
http://arxiv.org/abs/2201.05964
Publikováno v:
SIGMOD Conference
Computing technology has enabled massive digital traces of our personal lives to be collected and stored. These datasets play an important role in numerous real-life applications and research analysis, such as contact tracing for COVID 19, but they c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::224e422f69c0c508278c56ac031856a1
https://doi.org/10.1145/3448016.3457544
https://doi.org/10.1145/3448016.3457544
Publikováno v:
SIGMOD Conference
In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow appendonly data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to secur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcca9c99fb08512163771cb1d79ce1ad
Autor:
Kartik Nayak, Chenghong Wang, Ashwin Machanavajjhala, Johes Bater, Yanping Zhang, Matthew Lentz, Jun Yang, Lavanya Vasudevan, David Pujol
Publikováno v:
SenSys
Physical distancing between individuals is key to preventing the spread of a disease such as COVID-19. On the one hand, having access to information about physical interactions is critical for decision makers; on the other, this information is sensit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a3ff601458bc1a72ea25d1be9aa3b3f0
https://doi.org/10.1145/3384419.3430603
https://doi.org/10.1145/3384419.3430603
Publikováno v:
Proceedings of the VLDB Endowment. 12:307-320
A private data federation is a set of autonomous databases that share a unified query interface offering in-situ evaluation of SQL queries over the union of the sensitive data of its members. Owing to privacy concerns, these systems do not have a tru
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d76daf0905f234b82bec79b548524513
https://doi.org/10.14778/3291264.3291274
https://doi.org/10.14778/3291264.3291274
Publikováno v:
Heterogeneous Data Management, Polystores, and Analytics for Healthcare ISBN: 9783030337513
Poly/DMAH@VLDB
Poly/DMAH@VLDB
We are storing and querying datasets with the private information of individuals at an unprecedented scale in settings ranging from IoT devices in smart homes to mining enormous collections of click trails for targeted advertising. Here, the privacy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::22a89cee2ee3e6b62094038a19cbdeb1
https://doi.org/10.1007/978-3-030-33752-0_7
https://doi.org/10.1007/978-3-030-33752-0_7
People and machines are collecting data at an unprecedented rate. Despite this newfound abundance of data, progress has been slow in sharing it for open science, business, and other data-intensive endeavors. Many such efforts are stymied by privacy c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::17411e1081a8ef34c8176c0305462c3d