Privstream: Differentially private event detection on data streams
Publication
, Conference
Fanaeepour, M; Machanavajjhala, A
Published in: CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy
March 13, 2019
Event monitoring and detection in real-time systems is crucial. Protecting users’ data while reporting an event in almost real-time will increase the level of this challenge. In this work, we adopt the strong notion of differential privacy to private stream counting for event detection with the aim of minimizing false positive and false negative rates as our utility metrics.
Duke Scholars
Published In
CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy
DOI
ISBN
9781450360999
Publication Date
March 13, 2019
Start / End Page
145 / 147
Citation
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Fanaeepour, M., & Machanavajjhala, A. (2019). Privstream: Differentially private event detection on data streams. In CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy (pp. 145–147). https://doi.org/10.1145/3292006.3302379
Fanaeepour, M., and A. Machanavajjhala. “Privstream: Differentially private event detection on data streams.” In CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy, 145–47, 2019. https://doi.org/10.1145/3292006.3302379.
Fanaeepour M, Machanavajjhala A. Privstream: Differentially private event detection on data streams. In: CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy. 2019. p. 145–7.
Fanaeepour, M., and A. Machanavajjhala. “Privstream: Differentially private event detection on data streams.” CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy, 2019, pp. 145–47. Scopus, doi:10.1145/3292006.3302379.
Fanaeepour M, Machanavajjhala A. Privstream: Differentially private event detection on data streams. CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy. 2019. p. 145–147.
Published In
CODASPY 2019 - Proceedings of the 9th ACM Conference on Data and Application Security and Privacy
DOI
ISBN
9781450360999
Publication Date
March 13, 2019
Start / End Page
145 / 147