| Title | SegGuard: Segmentation-based Anonymization of Network Data in Clouds for Privacy-Preserving Security Auditing |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Authors | Oqaily, Momen, Jarraya, Yosr, Mohammady, Meisam, Majumdar, Suryadipta, Pourzandi, Makan, Wang, Lingyu, Debbabi, Mourad |
| Journal | IEEE Transactions on Dependable and Secure Computing |
| Pagination | 1–1 |
| ISSN | 2160-9209 |
| Keywords | Cloud computing security auditing, compositionality, data anonymization, encryption audits, Predictive Metrics, privacy preserving, pubcrawl, Resiliency |
| Abstract | Security auditing allows cloud tenants to verify the compliance of cloud infrastructure with respect to desirable security properties, e.g., whether a tenant's virtual network is properly isolated from other tenants' networks. However, the input to such an auditing task, such as the detailed topology of the underlying cloud infrastructure, typically contains sensitive information which a cloud provider may be reluctant to hand over to a third party auditor. Additionally, auditing results intended for one tenant may inadvertently reveal private information about other tenants, e.g., another tenant's VM is reachable due to a misconfiguration. How to anonymize both the input data and the auditing results in order to prevent such information leakage is a novel challenge that has received little attention. Directly applying most of the existing anonymization techniques to such a context would either lead to insufficient protection or render the data unsuitable for auditing. In this paper, we propose SegGuard, a novel anonymization approach that prevents cross-tenant information leakage through per-tenant encryption, and prevents information leakage to auditors through hiding real input segments among fake ones; in addition, applying property-preserving encryption in an innovative way enables SegGuard to preserve the data utility for auditing while mitigating semantic attacks. We implement SegGuard based on OpenStack, and evaluate its effectiveness and overhead using both synthetic and real data. Our experimental results demonstrate that SegGuard can reduce the information leakage to a negligible level (e.g., less than 1% for an adversary with 50% pre-knowledge) with a practical response time (e.g., 62 seconds to anonymize a cloud infrastructure with 25,000 virtual machines). |
| DOI | 10.1109/TDSC.2019.2957488 |
| Citation Key | oqaily_segguard_2019 |
SegGuard: Segmentation-based Anonymization of Network Data in Clouds for Privacy-Preserving Security Auditing