This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals noth...This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.展开更多
Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software （PLS）. Nowadays, PLS has become a serious and challenging probl...Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software （PLS）. Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories： one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net （PPN） which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.展开更多
基金National Key Research and Development Program of China(2021YFB3101402)National Natural Science Foundation of China(62202294)。
文摘This study constructs a function-private inner-product predicate encryption(FP-IPPE)and achieves standard enhanced function privacy.The enhanced function privacy guarantees that a predicate secret key skf reveals nothing about the predicate f,as long as f is drawn from an evasive distribution with sufficient entropy.The proposed scheme extends the group-based public-key function-private predicate encryption(FP-PE)for“small superset predicates”proposed by Bartusek et al.(Asiacrypt 19),to the setting of inner-product predicates.This is the first construction of public-key FP-PE with enhanced function privacy security beyond the equality predicates,which is previously proposed by Boneh et al.(CRYPTO 13).The proposed construction relies on bilinear groups,and the security is proved in the generic bilinear group model.
基金This work is supported by the National Natural Science Foundation of China under Grant Nos. 61402124, 61402022, 61173008, 60933005, and 61572469, the National Key Technology Research and Development Program of China under Grant No. 2012BAH39B02, the 242 Projects of China under Grant No. 2011F45, and Beijing Nova Program under Grant No. Z121101002512063.
文摘Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software （PLS）. Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories： one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net （PPN） which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.
