TY - GEN
T1 - SecLoc
T2 - 20th ACM Symposium on Access Control Models and Technologies, SACMAT 2015
AU - Li, Jingwei
AU - Squicciarini, Anna
AU - Lin, Dan
AU - Liang, Shuang
AU - Jia, Chunfu
N1 - Publisher Copyright:
© Copyright 2015 ACM.
PY - 2015/6/1
Y1 - 2015/6/1
N2 - Cloud computing offers a wide array of storage services. While enjoying the benefits of flexibility, scalability and reliability brought by the cloud storage, cloud users also face the risk of losing control of their own data, in partly because they do not know where their data is actually stored. This raises a number of security and privacy concerns regarding one's sensitive data such as health records. For example, according to Canadian laws, data related to personal identifiable information must be stored within Canada. Nevertheless, in contrast to the urgent demands, privacy requirements regarding to cloud storage locations have not been well investigated in the current cloud computing market, fostering security and privacy concerns among potential adopters. Aiming at addressing this emerging critical issue, we propose a novel secure location-sensitive storage framework, called SecLoc, which offers protection for cloud users' data following the storage location restrictions, with minimum management overhead to existing cloud storage services. We conduct security analysis, complexity analysis and experimental evaluation on the proposed SecLoc system. Our results demonstrate both effectiveness and efficiency of our mechanism.
AB - Cloud computing offers a wide array of storage services. While enjoying the benefits of flexibility, scalability and reliability brought by the cloud storage, cloud users also face the risk of losing control of their own data, in partly because they do not know where their data is actually stored. This raises a number of security and privacy concerns regarding one's sensitive data such as health records. For example, according to Canadian laws, data related to personal identifiable information must be stored within Canada. Nevertheless, in contrast to the urgent demands, privacy requirements regarding to cloud storage locations have not been well investigated in the current cloud computing market, fostering security and privacy concerns among potential adopters. Aiming at addressing this emerging critical issue, we propose a novel secure location-sensitive storage framework, called SecLoc, which offers protection for cloud users' data following the storage location restrictions, with minimum management overhead to existing cloud storage services. We conduct security analysis, complexity analysis and experimental evaluation on the proposed SecLoc system. Our results demonstrate both effectiveness and efficiency of our mechanism.
UR - http://www.scopus.com/inward/record.url?scp=84957713156&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84957713156&partnerID=8YFLogxK
U2 - 10.1145/2752952.2752965
DO - 10.1145/2752952.2752965
M3 - Conference contribution
AN - SCOPUS:84957713156
T3 - Proceedings of ACM Symposium on Access Control Models and Technologies, SACMAT
SP - 51
EP - 61
BT - SACMAT 2015 - Proceedings of the 20th ACM Symposium on Access Control Models and Technologies
PB - Association for Computing Machinery
Y2 - 1 June 2015 through 3 June 2015
ER -