Toward efficient and privacy-preserving computing in big data era
By Rongxing Lu, Hui Zhu, Ximeng Liu, Joseph K. Liu, Jun Shao. IEEE Network, 2014
349 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Information Systems
View Paper: https://www.semanticscholar.org/paper/e0b4ad70314fc511255bf97557b4c9bee77e04ae
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Hayat Khaloufi
- Abderrahim Beni Hssane
- Karim Abouelmehdi
- Rongxing Lu
- Muneeb Ul Hassan
- Jinjun Chen
- Tom H. Luan
- Mubashir Husain Rehmani
In Information Systems
- Hayat Khaloufi
- Abderrahim Beni Hssane
- Karim Abouelmehdi
- Rongxing Lu
- Muneeb Ul Hassan
- Jinjun Chen
- Tom H. Luan
- Mubashir Husain Rehmani
Citations in the corpus
- Critical analysis of Big Data challenges and analytical methods (2017, background): “• Security : Security is a major issue and is identi fi ed by Lu et al. (2014) who argue that if security challenges are not appropriately addressed then the phenomenon of BD will not receive much acceptance globally.”
- Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption (2016, background): “However, with the surging mobile traffic generated in recent years, the transmission of the extraordinarily hugevolume data to the cloud has not only posed a heavy burden on communication bandwidth, but also resulted in unbearable transmission latency and degraded service to end users [2]–[4].”
- Differential Privacy Techniques for Cyber Physical Systems: A Survey (2018, background): “nique used by majority of systems to protect the data from adversaries and unauthorized users [18], because it provides feature of data inaccessibility to unauthorized users.”
- Big healthcare data: preserving security and privacy (2018, background): “Paper [67] introduced also an efficient and privacy-preserving cosine similarity computing protocol and paper [68] discussed how an existing approach “differential privacy” is suitable for big data.”
- Big data privacy: a technological perspective and review (2016, methodology): “Privacy‐preserving aggregation Privacy-preserving aggregation [38] is built on homomorphic encryption used as a popular data collecting technique for event statistics.”