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Control-flow integrity (CFI) is considered a principled mitigation against control-flow hijacking even under the most powerful attacker who can arbitrarily write and read memory. However, existing schemes still demonstrated limitations in either guaranteeing high security level or achieving low performance and memory overhead. These limitations have restricted the application of CFI in real software.To improve its applicability similar to mandatory protection schemes such as DEP and ASLR, it is essential to improve both high security guarantee and low overhead. In this paper, we propose "BGCFI", which is a fine-grained CFI based on a Bipartite Graph. The relationship between an indirect branch and a valid target address at the branch is represented by an edge in the bipartite graph. The verification of the indirect branch is achieved by checking the existence of the corresponding edge in the bipartite graph. The verification method for fine-grained CFI results in more efficiency on both computational and memory overhead, while completely preserving high security guarantee. We demonstrate our results through the implementation of a proof-of-concept module and evaluation on the SPEC CPU 2017 suite and the Firefox browser.INDEX TERMS Control-flow hijacking, control-data attack, control-flow integrity (CFI).

Classification is used in various areas where k-nearest neighbor classification is the most popular as it produces efficient results. Cloud computing with powerful resources is one reliable option for handling large-scale data efficiently, but many companies are reluctant to outsource data due to privacy concerns. This paper aims to implement a privacy-preserving k-nearest neighbor classification (PkNC) in an outsourced environment. Existing work proposed a secure protocol (SkLE/SkSE) to compute k data with the largest/smallest value privately, but this work discloses information. Moreover, SkLE/SkSE requires a secure comparison protocol, and the existing protocols also contain information disclosure problems. In this paper, we propose a new secure comparison and SkLE/SkSE protocols to solve the abovementioned information disclosure problems and implement PkNC with these novel protocols. Our proposed protocols disclose no information and we prove the security formally. Then, through extensive experiments, we demonstrate that the PkNC applying the proposed protocols is also efficient. Especially, the PkNC is suitable for big data analysis to handle large amounts of data, since our SkLE/SkSE is executed for each dataset in parallel. Although the proposed protocols do require efficiency sacrifices to improve security, the running time of our PkNC is still significantly more efficient compared with previously proposed PkNCs.

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