His primary areas of investigation include Computer security, Internet privacy, Artificial intelligence, Inference and Machine learning. His work on Cryptographic protocol as part of general Computer security research is frequently linked to Genome, Personalized medicine and DNA sequencing, bridging the gap between disciplines. His work on Privacy by Design, Privacy software and Information privacy as part of general Internet privacy study is frequently connected to Aggression and Mainstream, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The Artificial intelligence study combines topics in areas such as Adversary and Collaborative learning. His Inference study integrates concerns from other disciplines, such as Overfitting, Stability, Generative grammar, Generative model and Discriminative model. His work in Fake news addresses subjects such as World Wide Web, which are connected to disciplines such as Perceptual hashing.
His primary scientific interests are in Computer security, Internet privacy, World Wide Web, Social media and Information sensitivity. His studies deal with areas such as Overhead and The Internet as well as Computer security. His research investigates the connection between Overhead and topics such as Aggregate that intersect with issues in Inference.
He frequently studies issues relating to Social network and Internet privacy. His studies in World Wide Web integrate themes in fields like Service provider, Timeline and Anonymity. His Social media research includes themes of Internet research and Public opinion.
Internet privacy, Social media, Set, Inference and Artificial intelligence are his primary areas of study. His Internet privacy study combines topics in areas such as Information warfare and Social network. His Social media research is included under the broader classification of World Wide Web.
His Inference research integrates issues from Quality, Computer security, Adversary, Data science and Robustness. He combines subjects such as False positive paradox and False positives and false negatives with his study of Computer security. While the research belongs to areas of Artificial intelligence, Emiliano De Cristofaro spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Risk assessment and Information leakage.
Emiliano De Cristofaro mainly investigates Machine learning, Artificial intelligence, Internet privacy, Social media and Inference. His biological study spans a wide range of topics, including Data modeling, Adversary, Training set and Generative grammar. His Artificial intelligence research is multidisciplinary, incorporating elements of Call graph and Android, Malware, Android malware.
His Internet privacy study incorporates themes from Relevance and Online harassment. His study with Social media involves better knowledge in World Wide Web. He usually deals with Inference and limits it to topics linked to Robustness and Federated learning, Differential privacy, Backdoor, Computer security and Leverage.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Exploiting Unintended Feature Leakage in Collaborative Learning
Luca Melis;Congzheng Song;Emiliano De Cristofaro;Vitaly Shmatikov.
ieee symposium on security and privacy (2019)
Practical private set intersection protocols with linear complexity
Emiliano De Cristofaro;Gene Tsudik.
financial cryptography (2010)
Mean Birds: Detecting Aggression and Bullying on Twitter
Despoina Chatzakou;Nicolas Kourtellis;Jeremy Blackburn;Emiliano De Cristofaro.
web science (2017)
Countering GATTACA: efficient and secure testing of fully-sequenced human genomes
Pierre Baldi;Roberta Baronio;Emiliano De Cristofaro;Paolo Gasti.
computer and communications security (2011)
Linear-complexity private set intersection protocols secure in malicious model
Emiliano De Cristofaro;Jihye Kim;Gene Tsudik.
international conference on the theory and application of cryptology and information security (2010)
Fast and Private Computation of Cardinality of Set Intersection and Union
Emiliano De Cristofaro;Paolo Gasti;Gene Tsudik.
cryptology and network security (2012)
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models
Enrico Mariconti;Lucky Onwuzurike;Panagiotis Andriotis;Emiliano De Cristofaro.
network and distributed system security symposium (2017)
LOGAN: Membership Inference Attacks Against Generative Models
Jamie Hayes;Luca Melis;George Danezis;Emiliano De Cristofaro.
privacy enhancing technologies (2019)
Privacy in content-oriented networking: threats and countermeasures
Abdelberi Chaabane;Emiliano De Cristofaro;Mohamed Ali Kaafar;Ersin Uzun.
acm special interest group on data communication (2013)
The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources
Savvas Zannettou;Tristan Caulfield;Emiliano De Cristofaro;Nicolas Kourtelris.
internet measurement conference (2017)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: