Dan Lin

110 papers receiving 3.1k citations

Dan Lin's Hit Papers

iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning 2016 · 284 citations
2840+3+6Years since publication50100150200250

Peers

Dan Lin
Comparison fields: 5 of 104
  • Signal Processing 779
  • Computer Networks and Communications 1.1k
  • Information Systems 1.0k
  • Artificial Intelligence 1.5k
  • Transportation 273
Replace Haibo Hu with:
Haibo Hu Hong Kong
Xiaofeng Meng China
Shojiro Nishio Japan
Mudhakar Srivatsa United States
Zhenjie Zhang China
Bolin Ding United States
Markus Miettinen Germany
Emanuele Della Valle Italy
Hakan Ferhatosmanoğlu United States
Özgür Ulusoy Türkiye
Dan Lin relative to Haibo Hu Hong Kong Haibo Hu's profile →
Citations per field
00.5×1.5×2×2.3×
Haibo Hu · 1×
Citations per year

Countries citing papers authored by Dan Lin

Since Specialization
Citations

This map shows the geographic impact of Dan Lin'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 Dan Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Lin more than expected).

Fields of papers citing papers by Dan Lin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dan Lin. 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 Dan Lin. The network helps show where Dan Lin may publish in the future.

Co-authors

The 25 scholars most cited alongside Dan Lin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dan Lin Line = papers co-authored together Dan Lin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 117 papers — load more, or switch the sort, to bring in the rest.

#Work
1
iPrivacy: Image Privacy Protection by Identifying Sensitive Objects via Deep Multi-Task Learning
Hit paper breakdown →
2016284
2 2004236
3 2016190
4 2018182
5 2012135
6 2012126
7 2008121
8 2007119
9 201376
10 200670
11 200970
12 200868
13 200966
14 200562
15 201460
16 201859
17 200756
18 201156
19 201252
20 201051

About Dan Lin

Dan Lin is a scholar working on Artificial Intelligence, Signal Processing, Information Systems, Computer Networks and Communications and Transportation, having authored 117 papers that have together received 3.3k indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (41 papers), Data Management and Algorithms (26 papers), Cryptography and Data Security (20 papers), Access Control and Trust (16 papers), Privacy, Security, and Data Protection (15 papers), Vehicular Ad Hoc Networks (VANETs) (15 papers), Cloud Data Security Solutions (15 papers) and Advanced Database Systems and Queries (13 papers). The work is most often cited by research in Signal Processing (779 citations), Computer Networks and Communications (1.1k citations), Information Systems (1.0k citations), Artificial Intelligence (1.5k citations) and Transportation (273 citations). Dan Lin has collaborated with scholars based in United States, China and Singapore. Frequent co-authors include Anna Squicciarini, Elisa Bertino, Christian S. Jensen, Smitha Sundareswaran, Beng Chin Ooi, Jun Yu, Baopeng Zhang, Jorge Lobo, Prathima Rao and Jianping Fan. Their work appears in journals such as IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Information Forensics and Security, Computers & Security and Soft Computing.

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.

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