Name
Abstract | ||
|---|---|---|
Many recent studies reveal that merely encrypting the source content does not inhibit the adversary from gaining knowledge about the source's behavior. Thus, the adversary might be able to extract information from the network traffic by employing statistical analysis. Based on the information theory, in this letter, we quantify the amount of knowledge obtained by an adversary that overhears the channel. We present an approach that mixes the features of applications in the source node, such that it maximizes the ambiguity of adversary. Finally, we suggest three lower bounds of adversary's error probability. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LCOMM.2016.2558182 | IEEE Communications Letters |
Keywords | Field | DocType |
Privacy,Error probability,Statistical analysis,Channel estimation,Cryptography,Feature extraction | Information theory,Cryptography,Adversary model,Computer science,Computer network,Communication channel,Encryption,Theoretical computer science,Adversary,Ambiguity,Advantage | Journal |
Volume | Issue | ISSN |
20 | 7 | 1089-7798 |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Hossein RezaeiTabar | 1 | 1 | 0.37 |
| Abolfazl Diyanat | 2 | 59 | 4.79 |
| Ahmad Khonsari | 3 | 210 | 42.43 |