PrivApprox: Privacy-Preserving Stream Analytics
نویسندگان
چکیده
منابع مشابه
PrivApprox: Privacy-Preserving Stream Analytics
How to preserve users’ privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three important properties: (i) Privacy: zero-knowledge privacy guarantee for users, a privacy bound tighter th...
متن کاملExplorer PrivApprox : Privacy - Preserving Stream Analytics
How to preserve users’ privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three important properties: (i) Privacy: zero-knowledge privacy guarantee for users, a privacy bound tighter th...
متن کاملPAS-MC: Privacy-preserving Analytics Stream for the Mobile Cloud
In today’s digital world, personal data is being continuously collected and analyzed without data owners’ consent and choice. As data owners constantly generate data on their personal devices, the tension of storing private data on their own devices yet allowing third party analysts to perform aggregate analytics yields an interesting dilemma. This paper introduces PAS-MC, the first practical p...
متن کاملQuantum Privacy-Preserving Data Analytics
Data analytics (such as association rule mining and decision tree mining) can discover useful statistical knowledge from a big data set. But protecting the privacy of the data provider and the data user in the process of analytics is a serious issue. Usually, the privacy of both parties cannot be fully protected simultaneously by a classical algorithm. In this paper, we present a quantum protoc...
متن کاملPrivacy-Preserving Distributed Stream Monitoring
Applications such as sensor network monitoring, distributed intrusion detection, and real-time analysis of financial data necessitate the processing of distributed data streams on the fly. While efficient data processing algorithms enable such applications, they require access to large amounts of often personal information, and could consequently create privacy risks. Previous works have studie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Informatik Spektrum
سال: 2019
ISSN: 0170-6012,1432-122X
DOI: 10.1007/s00287-019-01206-w