Privacy-enabled Recommendations for Software Vulnerabilities
Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding
Prioritizing vulnerabilities according to their relevance to the collection of software an organization uses is a costly and slow process.
While recommender systems were earlier proposed to address this issue, they ignore the security of the vulnerability prioritization data.
As a result, a malicious operator or a third party adversary can collect vulnerability prioritization data to identify the security assets in the enterprise deployments of client organizations.
To address this, we propose a solution that leverages isolated execution to protect the privacy of vulnerability profiles without compromising data integrity.
To validate an implementation of the proposed solution we integrated it with an existing recommender system for software vulnerabilities.
The evaluation of our implementation shows that the proposed solution can effectively complement existing recommender systems for software vulnerabilities.
|Title of host publication||The 17th IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2019)|
|Publisher||IEEE--Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Accepted/In press - 2019|
Related research output
Research output: Thesis › Doctoral Thesis (compilation)