TY - JOUR
T1 - Privacy-Preserving Fine-Grained Data Retrieval Schemes for Mobile Social Networks
AU - Mahmoud, Mohamed
AU - Rabieh, Khaled
AU - Sherif, Ahmed
AU - Oriero, Enahoro
AU - Ismail, Muhammad
AU - Serpedin, Erchin
AU - Qaraqe, Khalid
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.
AB - In this paper, we propose privacy-preserving fine-grained data retrieval schemes for mobile social networks (MSNs). The schemes enable users to retrieve data from other users who are interested in some topics related to a subject of interest. We define a subject to be a broad term that can cover many fine-grained topics, e.g., History can be a subject and World War I can be a topic. We consider centralized and decentralized network models. Our centralized scheme allows users to securely outsource data to a server such that the server matches the users who are interested in same topic(s) and have defined social attributes with privacy preservation. Searchable encryption scheme and a proposed cryptography construct are used to enable the server to match the topics and attributes without knowing any private information. By using the social attributes, users can prescribe the other users who can be connected to. We also propose a decentralized scheme that can be used when there is no connection to the server, i.e, shortage of Internet connectivity. The scheme leverages friends-of-friends relationship and transferable trust concept, where each user trusts his friends and the friends of friends. If a friend is not interested in the requested subject, he/she can link him/her to his/her friends without knowing the requested subject to preserve privacy. Our schemes use Bloom filters to store the topics of interest to reduce the storage and communication overhead. This is important because the number of fine-grained topics can be large. Different techniques to store the topics in the filter are proposed and investigated. Performance metrics are proposed and evaluated using real implementations. Our analysis and implementation results demonstrate that our schemes can preserve the privacy of the MSN users with high performance.
KW - data retrieval
KW - mobile social networks
KW - Privacy preservation
KW - profile matching
UR - http://www.scopus.com/inward/record.url?scp=85128491522&partnerID=8YFLogxK
U2 - 10.1109/TDSC.2017.2714162
DO - 10.1109/TDSC.2017.2714162
M3 - Article
AN - SCOPUS:85128491522
SN - 1545-5971
VL - 16
SP - 871
EP - 884
JO - IEEE Transactions on Dependable and Secure Computing
JF - IEEE Transactions on Dependable and Secure Computing
IS - 5
ER -