Cloud-assisted Internet of Things (IoT) is a new paradigm to compensate for the disadvantage of limited resources in IoT and extend the functional boundary of IoT. How to preserve the data privacy while identifying the data source in cloud-assisted IoT is a huge challenge and many cryptographic primitives such as matchmaking encryption (ME) are hence introduced to reach this goal. However, we observe that these tools fail to deal with some rigorous cases in which the data owner does not want anyone to disclose the entire data and even authorized users can only obtain a specified part. In this paper, to address such issue, we design a privacy-preserving data computing system with bilateral access control (PDCS-BAC) for cloud-assisted IoT based on a new primitive called identity-based matchmaking functional encryption (IB-MFE). We give the formal definition and security model of IB-MFE, then present two concrete IB-MFE schemes for inner-product function. Thus, PDCS-BAC manages to simultaneously provide flexible privacy computation with enhanced data privacy, bilateral access control, and data authenticity. Finally, we conduct comprehensive performance evaluations to demonstrate the practicability of our schemes for cloud-assisted IoT.

As one of the most common ways for user authentication, Personal Identification Number (PIN), due to its simplicity and convenience, has suffered from plenty of side-channel attacks, which pose a severe threat to people’s privacy and property. The success of existing attacks is usually built upon the premise of no occlusion between the attacker and the victim’s hand gesture, but it increases the difficulty of launching the attack and the possibility of exposure. To overcome such limitation, we propose ArmSpy++, an improved video-assisted PIN inference attack built upon our previous research, ArmSpy. Specifically, ArmSpy++ employs new modules to leverage more features like the keystroke-induced elbow bending, wrist speed variation, and the spatial relationship between different arm joints, to correctly detect Keystrokes. ArmSpy++ delves into the perspective relationship and natural typing habits to ensure a high success rate of PIN inference. We also re-designed the inferred PIN pattern coordination mechanism to accurately deduce the PINs. By using a pre-trained HigherHRNet model for posture estimation ArmSpy++ eliminates the necessity of additional training. The extensive experiments demonstrate that ArmSpy++ can achieve over 83.1% average accuracy with 3 attempts and even 92.5% for some victims, indicating the severity of the threat posed by ArmSpy++.

Group signature with verifier-local revocation (VLR-GS) is a special variant of revocable group signature that not only allows a user to anonymously sign messages but also only requires the verifiers to possess some up-to-date revocation information. To date, a number of VLR-GS schemes have been proposed under bilinear groups and lattices, while they have not yet been instantiated based on coding theory. In this paper, we present a code-based VLR-GS scheme in the random oracle model, which is the first construction to the best of our knowledge. Concretely, our VLR-GS scheme does not rely on the traditional paradigm which utilizes an encryption scheme as a building block and achieves logarithmic-size group signature. To obtain the scheme, we first introduce a new code-based Stern-like interactive zero-knowledge protocol with member revocation mechanism based on syndrome decoding problem. Moreover, we employ the binary Goppa code embedded for our scheme with efficiency and security analysis.