Demo Data from VLDB 2026 / reviewers in the wild / expert
Daniela Lopes
dblp:332/3062
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2024
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
2 papers |
Network security · 100% | |
| Computer networks
1 paper |
Network measurement and analytics · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › traffic analysis
flow correlation attack |
1.3 | 2 | 2024 | Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum · NDSS 2024 Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? · CCS 2022 |
Network security
traffic analysis |
0.8 | 2 | 2024 | Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? · CCS 2022 Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum · NDSS 2024 |
Network security › anonymity networks
anonymity network attack |
0.8 | 1 | 2024 | Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum · NDSS 2024 |
Network security
anonymity networks |
0.6 | 1 | 2022 | Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? · CCS 2022 |
Network security › anonymity networks
hidden service deanonymization |
0.6 | 1 | 2022 | Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? · CCS 2022 |
Network security › anonymity networks
tor |
0.6 | 1 | 2022 | Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? · CCS 2022 |
Network measurement and analytics
traffic classification |
0.2 | 1 | 2022 | Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale? · CCS 2022 |
Methods — techniques the papers use, named apart from their topics
traffic correlation · 1.1traffic classification · 1.1sliding subset sum · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Flow Correlation Attacks on Tor Onion Service Sessions with Sliding Subset Sum
Daniela Lopes, Jin-Dong Dong, Pedro Medeiros, Daniel Castro 0004, Diogo Barradas, Bernardo Portela, João Vinagre, Bernardo Ferreira, Nicolas Christin, Nuno Santos 0001 |
NDSS | 1 |
| 2022 | Poster: User Sessions on Tor Onion Services: Can Colluding ISPs Deanonymize Them at Scale?abstractTor is the most popular anonymity network in the world. It relies on advanced security and obfuscation techniques to ensure the privacy of its users and free access to the Internet. However, the investigation of traffic correlation attacks against Tor Onion Services (OSes) has been relatively overlooked in the literature. In particular, determining whether it is possible to emulate a global passive adversary capable of deanonymizing the IP addresses of both the Tor OSes and of the clients accessing them has remained, so far, an open question. In this paper, we present ongoing work toward addressing this question and reveal some preliminary results on a scalable traffic correlation attack that can potentially be used to deanonymize Tor OS sessions. Our attack is based on a distributed architecture involving a group of colluding ISPs from across the world. After collecting Tor traffic samples at multiple vantage points, ISPs can run them through a pipeline where several stages of traffic classifiers employ complementary techniques that result in the deanonymization of OS sessions with high confidence (i.e., low false positives). We have responsibly disclosed our early results with the Tor Project team and are currently working not only on improving the effectiveness of our attack but also on developing countermeasures to preserve Tor users' privacy. Daniela Lopes, Pedro Medeiros, Jin-Dong Dong, Diogo Barradas, Bernardo Portela, João Vinagre, Bernardo Ferreira, Nicolas Christin, Nuno Santos 0001 |
CCS | 1 |