Side-Channel Attack on Web
Counteracting Web Malvertising
Secure Computing on Hybrid Clouds
Secure Web Commerce
Mobile Fragmentation

CNS-1223495: TWC: Small: Secure Data-Intensive Computing on Hybrid Clouds


The ongoing effort to move data intensive computation to low-cost public clouds has been impeded by privacy concerns, as today's cloud providers offer little assurance for the protection of sensitive user data. This problem cannot be addressed by existing cryptographic techniques alone, which are often too heavyweight to manage the computation involving a large amount of data. As a result, many computing tasks have to be run on individual organizations’ internal systems whenever they touch even a very small amount of sensitive information. The research in this project seeks practical solutions to this critical security challenge, using computation partition over a hybrid-cloud platform together with lightweight cryptography techniques. It involves an in-depth understanding of potential security threats to this new hybrid-cloud computing infrastructure and development of effective and scalable technologies to address these threats, making secure data-intensive computing feasible on the cloud. This project involves industry collaborators and contributes to secure processing of a wide range of computing jobs, from commercial data analysis, to DNA analysis, to intrusion detection.

Related paper

  • V. Bindschadler, Y. Chen and X. Wang, 2014, SecAV: Lightweight Privacy-Preserving Malware Scanning on Untrusted Mobile Clouds.
  • T. Li, X. Zhou, L. Xing, Y. Lee, M. Naveed, X. Wang and X. Han, 2014, Mayhem in the Push Clouds: Understanding and Mitigating Security Hazards in Mobile Push-Messaging Services. accepted by the 21st ACM Conference on Computer and Communications Security (CCS).
  • M. Naveed, S. Agrawal, M. Prabhakaran, X. Wang, E. Ayday, JP Hubaux and C. Gunter, Controlled Functional Encryption. accepted by the 21st ACM Conference on Computer and Communications Security (CCS)
  • Y. Chen, B. Peng, X. Wang and H. Tang, 2012 “Large-Scale Privacy-Preserving Mappings of Human Genomic Sequences on Hybrid Clouds”. In Proceedings of the 19th Annual Network and Distributed System Security Symposium (NDSS)
  • K. Zhang, X. Zhou, Y. Chen, X. Wang and Y. Ruan, 2011 “Sedic: Privacy-Aware Data Intensive Computing on Hybrid Clouds”. In Proceedings of the 18th ACM Conference on Computer and Communications Security (CCS)


  • X. Wang, H. Tang, Y. Chen and B. Peng, “Secure and Scalable Mapping of Human Sequencing Reads on Hybrid Clouds”. Pending, Patent Application filed by Indiana University on November 7th, 2013

Other Resources

  • Under construction