The term “metaverse”, a three-dimensional virtual universe similar to the real realm, has always been full of imagination since it was put forward in the 1990s. Recently, it is possible to realize the metaverse with the continuous emergence and progress of various technologies, and thus it has attracted extensive attention again. It may bring a lot of benefits to human society such as reducing discrimination, eliminating individual differences, and socializing. However, everything has security and privacy concerns, which is no exception for the metaverse. In this article, we firstly analyze the concept of the metaverse and propose that it is a super virtual-reality (VR) ecosystem compared with other VR technologies. Then, we carefully analyze and elaborate on possible security and privacy concerns from four perspectives: user information, communication, scenario, and goods, and immediately, the potential solutions are correspondingly put forward. Meanwhile, we propose the need to take advantage of the new buckets effect to comprehensively address security and privacy concerns from a philosophical perspective, which hopefully will bring some progress to the metaverse community.

Recently, Rahulamathavan et al. propose a privacy preserving scheme for outsourcing SVM classification. Their core contribution is a secure protocol to attain the sign of numbers in encrypted form. In this paper, we observe that Rahulamathavan et al.'s protocol will suffer from some soundness and security problems. Then, we propose a new scheme to securely obtain the encrypted numbers' sign. Theoretical analysis and experiment results show our proposed scheme can not only fix the soundness and security problems, but also achieve higher efficiency.

The tremendous progress in deep learning has enabled to extract soft-biometric attributes from faces, which raises privacy concerns over images collected for face recognition. Advances toward attribute privacy have been able to conceal multiple attributes while preserving identity information but suffer from limitations: they 1) only consider a few soft-biometric attributes and 2) fail to support reversibility for attribute privacy preservation. To break these limitations, we design a reversible privacy-preserving scheme for various face attributes, called reversible attribute privacy preservation (RAPP). RAPP benefits from two modules: 1) The attribute obfuscator introduces a stream cipher to determine that special attributes have to be concealed with the user-defined password, which also supports recovering original attributes. 2) The attribute adversarial network is proposed to generate perturbed images that conceal various attributes while retaining the utility of face verification. In addition, when a wrong password is provided, the returned image with wrong attribute classification results still keeps realistic, which confuses an attacker to know whether the recovery is correct. Extensive experiments demonstrate that RAPP enables to conceal various attributes and recover original images while facilitating face verification.

In the age of the industrial big data, there are several significant problems such as high-overhead data acquisition, data privacy leakage, and data tampering. Fog computing capability is rapidly expanding to address not only network congestion issues but data security issues. This paper presents a chaotic compressive sensing (CS) scheme for securely processing industrial big image data in the fog computing paradigm, called Privacy-Assured FogCS. Specially, the Sine Logistic modulation map is used to drive the privacy-assured, authenticated, and block CS for secure image data collection in the sensor nodes. After sampling, the measurements are normalized in the fog nodes. Finally, these relevant data are transmitted to the clouds (data centers) for storage, reconstruction, and authentication if required. In addition, a hardware implementation reference on a field programmable gate array (FPGA) is designed. Simulation analyses show the feasibility and efficiency of the Privacy-Assured FogCS scheme.

Naïve Bayes classifier (NBC) is a fundamental and widely-used data mining tool. To respond to the growing privacy concern, several privacy-preserving NBC schemes have been proposed recently. Generally, the existing schemes mainly exploit two different techniques: one is secure multi-party computation (SMCP); and the other is (local) differential privacy (DP/LDP). However, the approaches with SMCP would employ some encryption algorithms, which results in heavy computation and communication costs and impedes the usage of the approaches in practice. The existing methods with DP/LDP also have some deficiencies. For instance, a trusted data curator is required or the class labels of individuals are revealed. To make up the drawbacks of previous schemes, in this paper, we develop a novel scheme to train a Naïve Bayes classifier with privacy guarantee in the local setting. We first design a method (JESS) for joint distribution estimation under LDP. Then we apply JESS to calculate the conditional probability, the key of the Naïve Bayes classification and train a Naïve Bayes classifier. Additionally, we leverage extensive experiments to evaluate the effectiveness of our schemes designed for joint distribution and Naïve Bayes classification. Compared with the existing schemes, ours can achieve higher accuracy.