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Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security
Abstract
As the ACM's flagship security and privacy conference for more than two decades and the world's largest forum on computing and communications security research, CCS today is facing great challenges in meeting the demands of an expanding community and also ensuring high quality of its published research. This year's conference is unique as the last one before the conference's model change, moving from a single deadline toward a multiple deadline and more journal-like system. This year, a total of 809 submissions were received by the conference. Among them, 134 were selected to appear in the program and be included in the proceedings, with an acceptance rate of 16.6%. These papers were reviewed by a Program Committee of 159 security and privacy experts from academia, industry and government around the world. Following the convention in the past years, the review process includes three rounds. Each submission were reviewed by two PC members in the first round and those moving to the later rounds received additional reviews, depending on the results of the initial reviews. The vast majority of the accepted papers have 4 to 5 reviews. Right after the second round, authors were given the opportunities to respond to the reviews received. During the final review stage, papers were extensively discussed online, taking into account both reviews and the author responses, and additional reviews were assigned to those controversial or with low confident reviews only. Like last year, we used Toronto Paper Matching System (TPMS) to automatically assign papers and then manually adjusted the assignments when necessary. Most PC members have reviewed 16 to 20 papers. The accepted papers cover a large spectrum of security and privacy topics, ranging from cryptography and formal methods to SDN, blockchain and IoT security. This diversity demonstrates the breadth of applied security and privacy research and also reflects the technological trends in the area.
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Cited by
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Federated learning on non-IID data: A survey
Zhu, H., Xu, J., Liu, S., & Jin, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.07.098 · Cited by 1,062
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Banabilah, S., Aloqaily, M., Alsayed, E., Malik, N., & Jararweh, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103061 · Cited by 556
Secure Multi-Party Computation: Theory, practice and applications
Zhao, C., Zhao, S., Zhao, M., Chen, Z., Gao, C.-Z., Li, H., & Tan, Y.-A. (2019) · Information Sciences · doi:10.1016/j.ins.2018.10.024 · Cited by 508
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Metadata
- Publisher
- ACM
- Published
- 2018-10-15
- OpenAlex
- W4315746341
- Cited by
- 1,165
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
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