High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society; however, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on the expectation maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a local differentially private high-dimensional data publication algorithm (LoPub) by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on real-world datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of support vector machine and random forest classification, respectively.

Cloud storage services enable individuals and organizations to outsource data storage to remote servers. Cloud storage providers generally adopt data deduplication, a technique for eliminating redundant data by keeping only a single copy of a file, thus saving a considerable amount of storage and bandwidth. However, an attacker can abuse deduplication protocols to steal information. For example, an attacker can perform the duplicate check to verify whether a file (e.g., a pay slip, with a specific name and salary amount) is already stored (by someone else), hence breaching the user privacy. In this paper, we propose ZEUS (zero-knowledge deduplication response) framework. We develop ZEUS and ZEUS+, two privacy-aware deduplication protocols: ZEUS provides weaker privacy guarantees while being more efficient in the communication cost, while ZEUS+ guarantees stronger privacy properties, at an increased communication cost. To our knowledge, ZEUS is the first solution which addresses two-side privacy by neither using any extra hardware nor depending on heuristically chosen parameters used by the existing solutions, thus reducing both cost and complexity of the cloud storage. We show the efficiency of the proposed framework by evaluating on real dataset and comparing the communication cost of the proposed solutions, and prove the privacy.

Autonomous Vehicles (AVs) are equipped with several sensors which produce various forms of data, such as geo-location, distance, and camera data. The volume and utility of these data, especially camera data, have contributed to the advancement of high-performance self-driving applications. However, these vehicles and their collected data are prone to several security and privacy attacks. One of the main attacks against the AV-generated camera data is location inference, in which camera data is used to extract knowledge for tracking the users. A few research studies have proposed privacy-preserving approaches for analysing AV-generated camera data using powerful generative models, such as Variational Auto Encoder (VAE) and Generative Adversarial Network (GAN). However, the related work considers a weak geo-localisation attack model, which leads to weak privacy protection against stronger attack models. In this paper, we propose DeepClean, a robust deep learning model that combines VAE and a private clustering technique. DeepClean learns distinct labelled object structures of the image data as a cluster, and generates a more visual representation of the non-private object clusters, e.g., roads. It then distorts the private object areas using a private Gaussian Mixture Model (GMM) to learn distinct cluster structures of the labelled object areas. The synthetic images generated from our model guarantee privacy and resist against a robust location inference attack by less than 4% localisation accuracy. This implies that using DeepClean for synthetic data generation, it is less likely for a subject to be localised by an attacker even when using a robust geo-localisation attack. The overall image utility level of the generated synthetic images by DeepClean is comparable to the benchmark studies.

Cloning is a very serious threat in the Internet of Things (IoT), owing to the simplicity for an attacker to gather configuration and authentication credentials from a non-tamper-proof node, and replicate it in the network. In this paper, we propose MDSClone, a novel clone detection method based on multidimensional scaling (MDS). MDSClone appears to be very well suited to IoT scenarios, as it (i) detects clones without the need to know the geographical positions of nodes, and (ii) unlike prior methods, it can be applied to hybrid networks that comprise both static and mobile nodes, for which no mobility pattern may be assumed a priori. Moreover, a further advantage of MDSClone is that (iii) the core part of the detection algorithm can be parallelized, resulting in an acceleration of the whole detection mechanism. Our thorough analytical and experimental evaluations demonstrate that MDSClone can achieve a 100% clone detection probability. Moreover, we propose several modifications to the original MDS calculation, which lead to over a 75% speed up in large scale scenarios. The demonstrated efficiency of MDSClone proves that it is a promising method towards a practical clone detection design in IoT.

As the number of everyday objects connected to the Internet grows rapidly, securing these connected devices is a big security challenge. Key establishment in Internet of Things (IoT) becomes a challenging problem when considering the resource constrained sensor nodes. In spite of the fact that many clever solutions have been proposed, no practical and suitable scheme has emerged, especially for the extremely large amount of sensor nodes in the wireless sensor network (WSNs) in the future. In this paper, we propose a new key establishment scheme for IoT. The scheme is achieved by Kronecker product and satisfies the following conditions. 1) Substantially decreases the amount of data needs to be stored in a sensor node, 2) efficiently compute the pairwise key, 3) no communication is needed during the computation of the keys. The security evaluation is performed and we also present an in depth analysis of our scheme in terms of computation cost, communication cost and storage cost.