In the context of healthcare 4.0, cloud‐based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper‐proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy‐preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state‐of‐the‐art ones.

With the development of speech steganography technology, steganographers are more and more inclined to realize more secure covert communication by combining a series of steganography methods. Thus, this letter presents a novel multi-encoder network (MENet) to achieve more efficient detection of multiple steganography methods. Differing from the previous work, MENet utilizes multiple private encoders to individually model the private features of each coding element, introduces a shared encoder based on an attention mechanism to fuse multiple private features for achieving better feature representation, and finally exploits a shared decoder to reduce feature dimensionality as well as give predictions. Taking the existing state-of-the-art steganography methods as the detection targets, the performance of the proposed steganalysis method is evaluated comprehensively and compared with the state-of-the-art ones. The experimental results show that the detection performance of MENet is overall better than the existing steganalysis methods, especially with low embedding rates and short speech sample lengths.

The Internet of Vehicles (IoV) , with the help of cloud computing, can provide rich and powerful application services for vehicles and drivers by sharing and analysing various IoV data. However, how to ensure the integrity of IoV data with multiple sources and diversity outsourced in the cloud is still an open challenge. To address this concern, this paper first presents an identity-based public auditing scheme for cloud storage of IoV data, which can fully achieve the essential function and security requirements, such as classified auditing, multi-source auditing and privacy protection. Particularly, we design a new authenticated data structure, called data mapping table, to track the distribution of each type of IoV data to ensure fine and rapid audits. Moreover, our scheme can reduce the overheads for both the key management and the generation of block tags. We formally prove the security of the presented scheme and evaluate its performance by comprehensive comparisons with the state-of-the-art schemes designed for traditional scenarios. The theoretical analyses and experimental results demonstrate that our scheme can securely and efficiently realize public auditing for IoV data, and outperforms the previous ones in both the computation and communication overheads in most cases.

Due to increasing concerns of data privacy, databases are being encrypted before they are stored on an untrusted server. To enable search operations on the encrypted data, searchable encryption techniques have been proposed. Representative schemes use order-preserving encryption (OPE) for supporting efficient Boolean queries on encrypted databases. Yet, recent works showed the possibility of inferring plaintext data from OPE-encrypted databases, merely using the order-preserving constraints, or combined with an auxiliary plaintext dataset with similar frequency distribution. So far, the effectiveness of such attacks is limited to single-dimensional dense data (most values from the domain are encrypted), but it remains challenging to achieve it on high-dimensional datasets (e.g., spatial data), which are often sparse in nature. In this paper, for the first time, we study data inference attacks on multi-dimensional encrypted databases (with 2-D as a special case). We formulate it as a 2-D order-preserving matching problem and explore both unweighted and weighted cases, where the former maximizes the number of points matched using only order information and the latter further considers points with similar frequencies. We prove that the problem is NP-hard, and then propose a greedy algorithm, along with a polynomial-time algorithm with approximation guarantees. Experimental results on synthetic and real-world datasets show that the data recovery rate is significantly enhanced compared with the previous 1-D matching algorithm.

With the ubiquitous adoption of smartphones and mobile devices, it is now common practice for one's location to be sensed, collected and likely shared through social platforms. While such data can be helpful for many applications, users start to be aware of the privacy issue in handling location and trajectory data. While some users may voluntarily share their location information (e.g., for receiving location-based services, or for crowdsourcing systems), their location information may lead to information leaks about the whereabouts of other users, through the co-location of events when two users are at the same location at the same time and other side information, such as upper bounds of movement speed. It is therefore crucial to understand how much information one can derive about other's positions through the co-location of events and occasional GPS location leaks of some of the users. In this paper we formulate the problem of inferring locations of mobile agents, present theoretically-proven bounds on the amount of information that could be leaked in this manner, study their geometric nature, and present algorithms matching these bounds. We will show that even if a very weak set of assumptions is made on trajectories' patterns, and users are not obliged to follow any 'reasonable' patterns, one could infer very accurate estimation of users' locations even if they opt not to share them. Furthermore, this information could be obtained using almost linear-time algorithms, suggesting the practicality of the method even for huge volumes of data.

Uploading data streams to a resource-rich cloud server for inner product evaluation, an essential building block in many popular stream applications (e.g., statistical monitoring), is appealing to many companies and individuals. On the other hand, verifying the result of the remote computation plays a crucial role in addressing the issue of trust. Since the outsourced data collection likely comes from multiple data sources, it is desired for the system to be able to pinpoint the originator of errors by allotting each data source a unique secret key, which requires the inner product verification to be performed under any two parties' different keys. However, the present solutions either depend on a single key assumption or powerful yet practically-inefficient fully homomorphic cryptosystems. In this paper, we focus on the more challenging multi-key scenario where data streams are uploaded by multiple data sources with distinct keys. We first present a novel homomorphic verifiable tag technique to publicly verify the outsourced inner product computation on the dynamic data streams, and then extend it to support the verification of matrix product computation. We prove the security of our scheme in the random oracle model. Moreover, the experimental result also shows the practicability of our design.