| Title |
Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation
|
|---|---|
| Published in |
EURASIP Journal on Information Security, June 2020
|
| DOI | 10.1186/s13635-020-00106-x |
| Pubmed ID | |
| Authors |
Olga Taran, Shideh Rezaeifar, Taras Holotyak, Slava Voloshynovskiy |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| France | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 13% |
| Researcher | 2 | 9% |
| Other | 1 | 4% |
| Lecturer | 1 | 4% |
| Professor | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 12 | 52% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 30% |
| Business, Management and Accounting | 1 | 4% |
| Unspecified | 1 | 4% |
| Immunology and Microbiology | 1 | 4% |
| Engineering | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 12 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 April 2021.
All research outputs
#20,667,544
of 25,387,668 outputs
Outputs from EURASIP Journal on Information Security
#64
of 81 outputs
Outputs of similar age
#332,874
of 433,033 outputs
Outputs of similar age from EURASIP Journal on Information Security
#4
of 5 outputs
Altmetric has tracked 25,387,668 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 81 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 433,033 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.