Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
2019888 citationsMilad Nasr, Reza Shokri et al.arXiv (Cornell University)profile →
Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning
Countries citing papers authored by Amir Houmansadr
Since
Specialization
Citations
This map shows the geographic impact of Amir Houmansadr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Amir Houmansadr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amir Houmansadr more than expected).
This network shows the impact of papers produced by Amir Houmansadr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Amir Houmansadr. The network helps show where Amir Houmansadr may publish in the future.
Co-authorship network of co-authors of Amir Houmansadr
This figure shows the co-authorship network connecting the top 25 collaborators of Amir Houmansadr.
A scholar is included among the top collaborators of Amir Houmansadr based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Amir Houmansadr. Amir Houmansadr is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nasr, Milad, et al.. (2021). Defeating DNN-Based Traffic Analysis Systems in Real-Time With Blind Adversarial Perturbations.. USENIX Security Symposium. 2705–2722.21 indexed citations
6.
Gill, Phillipa, et al.. (2020). Triplet Censors: Demystifying Great Firewall’s {DNS} Censorship Behavior. USENIX Security Symposium.12 indexed citations
7.
Houmansadr, Amir, et al.. (2019). On the Importance of Encrypted-SNI ({ESNI}) to Censorship Circumvention. USENIX Security Symposium.15 indexed citations
8.
Nasr, Milad, Reza Shokri, & Amir Houmansadr. (2019). Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. arXiv (Cornell University). 739–753.888 indexed citations breakdown →
Nasr, Milad, Reza Shokri, & Amir Houmansadr. (2018). Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks.. arXiv (Cornell University).51 indexed citations
Houmansadr, Amir, et al.. (2016). Achieving perfect location privacy in Markov models using anonymization. International Symposium on Information Theory and its Applications. 355–359.6 indexed citations
Houmansadr, Amir, Thomas Riedl, Nikita Borisov, & Andrew C. Singer. (2013). I want my voice to be heard: IP over Voice-over-IP for unobservable censorship circumvention.. Network and Distributed System Security Symposium.54 indexed citations
Houmansadr, Amir. (2012). Design, analysis, and implementation of effective network flow watermarking schemes.2 indexed citations
18.
Houmansadr, Amir & Nikita Borisov. (2011). SWIRL: A Scalable Watermark to Detect Correlated Network Flows.. Network and Distributed System Security Symposium.60 indexed citations
19.
Houmansadr, Amir, Negar Kiyavash, & Nikita Borisov. (2009). RAINBOW: A Robust And Invisible Non-Blind Watermark for Network Flows.. Network and Distributed System Security Symposium.74 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.