In the era of expanding data volumes, users tend to encrypt data and upload their data to cloud servers. Broadcast encryption (BE) facilitates one to many secure data sharing and enjoys widespread adoption in many areas (e.g, Pay-TV, digital rights management, cloud storage). Nevertheless, the Snowden revelation in 2013 showed that attackers (e.g., manufacturers and supply-chain intermediaries) could tamper cryptographic implementations and insert backdoors to undermine cryptographic primitives. In this paper, we investigate the feasibility of algorithm substitution attacks (ASAs) on BE and formalize the corresponding adversarial model. Our findings reveal that attackers not only recover a user’s private key via public parameters but also extract a session key via any two consecutive headers, where the session key is encapsulated in the headers. Consequently, our ASAs pose a greater threat compared to previous ASAs targeting encryption primitives. To mitigate this problem, we adopt cryptographic reverse firewall (RF) as a countermeasure to achieve the security of BE against ASAs. We introduce the system model and security model of broadcast encryption with reverse firewalls (BE-RFs). Additionally, we construct an instance and provide a proof that our BE-RFs not only ensures selective identities and chosen plaintext secure (sID-CPA) but also is against ASAs. Subsequently, we implement BE-RFs on both Android devices and computers for experimental evaluations, confirming its efficacy in safeguarding security. Finally, we apply BE-RFs to data sharing in cloud storage.

Multimodal handwritten signatures usually involve offline images and online sequences. Since in real-world scenarios, different modalities of the same signature are generated simultaneously, most research hypothesizes that the different modalities are consistent. However, attacks launched on a partial modality (e.g., only tampering on the image modality) of signature data are commonly seen, and will cause the inter-modal inconsistency. In this paper, we propose and analyze the multimodal security and attack levels for handwritten signatures, and provide a multimodal consistency learning method to detect different levels of attacks of signatures. The modalities include not only traditional offline and online data, but also videos capturing hand movements. We collect a number of triple modal signatures to address the scarcity of public handwritten video datasets. Then, we extract hand joint sequences from videos and utilize them to analyze subtle multimodal consistency with the online modality. We provide extensive experiments for the consistency between online and offline signatures, as well as between online signatures and movement videos. The verification involves distance-based and classification-based fusion models, showing the most effective discriminative networks for attack detection and the superiority of consistency learning.