Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid development of DNNs has benefited from the existence of some high-quality datasets ( e.g ., ImageNet), which allow researchers and developers to easily verify the performance of their methods. Currently, almost all existing released datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes without permission. However, there is still no good way to ensure that. In this paper, we formulate the protection of released datasets as verifying whether they are adopted for training a (suspicious) third-party model, where defenders can only query the model while having no information about its parameters and training details. Based on this formulation, we propose to embed external patterns via backdoor watermarking for the ownership verification to protect them. Our method contains two main parts, including dataset watermarking and dataset verification. Specifically, we exploit poison-only backdoor attacks ( e.g ., BadNets) for dataset watermarking and design a hypothesis-test-guided method for dataset verification. We also provide some theoretical analyses of our methods. Experiments on multiple benchmark datasets of different tasks are conducted, which verify the effectiveness of our method. The code for reproducing main experiments is available at https://github.com/THUYimingLi/DVBW.

Federated learning (FL) enables data owners to train a global model with shared gradients while keeping private training data locally. However, recent research demonstrated that the adversary may infer private training data of clients from the exchanged local gradients, e.g., having deep leakage from gradients (DLGs). Many existing privacy-preserving approaches take usage of differential privacy (DP) to guarantee privacy. Nevertheless, the widely used privacy budget of DP (e.g., evenly distribution) leads to a sharp decline of model accuracy. To improve the model accuracy, some schemes only consider allocating the privacy budget to the fully connected layers. However, we reveal that the adversary may still reconstruct the private training data by adopting the DLG attack with the gradients of convolutional layers. In this article, we propose a fine-grained DP federated learning (DPFL) scheme, which guarantees privacy and remains high model performance simultaneously. Specifically, inspired by the methods that measure the importance of layers in deep learning, we propose a fine-grained method to allocate noise according to the importance value of layers in order to remain high model performance. Besides, we combine an active client selection strategy with DPFL and perform fine-tuning with a public data set on the server to further ensure the model performance. We evaluate DPFL under both independent and identically distributed (i.i.d) and non-i.i.d data settings to show that our method can achieve similar accuracy as the plain FL (e.g., FedAvg). We also demonstrate that our DPFL can resist the DLG attack to verify its privacy guarantee.

Cloud storage as one of the most important services of cloud computing facilitates cloud users to outsource their data to the cloud for storage and share them with authorized users. In cloud storage, secure deduplication has been investigated as it can eliminate the redundancy over the encrypted data to reduce storage space and communication overhead. Regarding the security and privacy, many existing secure deduplication schemes generally focus on achieving the following properties: data confidentiality, tag consistency, access control and resistance to brute force attacks. However, as far as we know, none of them can achieve these four requirements at the same time. To overcome this shortcoming, in this paper, we propose an efficient secure deduplication scheme that supports user-defined access control. Specifically, by allowing only the cloud service provider to authorize data access on behalf of data owners, our scheme can maximally eliminate duplicates without violating the security and privacy of cloud users. Detailed security analysis shows that our authorized secure deduplication scheme achieves data confidentiality and tag consistency while resisting brute-force attacks. Furthermore, extensive simulations demonstrate that our scheme outperforms the existing competing schemes, in terms of computational, communication and storage overheads as well as the effectiveness of deduplication.

Secure data deduplication can significantly reduce the communication and storage overheads in cloud storage services, and has potential applications in our big data-driven society. Existing data deduplication schemes are generally designed to either resist brute-force attacks or ensure the efficiency and data availability, but not both conditions. We are also not aware of any existing scheme that achieves accountability, in the sense of reducing duplicate information disclosure (e.g., to determine whether plaintexts of two encrypted messages are identical). In this paper, we investigate a three-tier cross-domain architecture, and propose an efficient and privacy-preserving big data deduplication in cloud storage (hereafter referred to as EPCDD). EPCDD achieves both privacy-preserving and data availability, and resists brute-force attacks. In addition, we take accountability into consideration to offer better privacy assurances than existing schemes. We then demonstrate that EPCDD outperforms existing competing schemes, in terms of computation, communication and storage overheads. In addition, the time complexity of duplicate search in EPCDD is logarithmic.

Federated learning enables clients to train models locally and provide local updates to the server instead of raw dataset, thereby preserving data privacy to some extent. However, adversaries can still pry users’ privacy by inferring updates, and compromise the integrity of the global model through poisoning attack. Therefore, many related works have integrated poisoning attack detection method with secure computation to address both issues. Nevertheless, they still encounter two major challenges: (i) the efficiency is too low to be applied in practice, and (ii) the privacy is still at risk of being leaked, e.g., the distance of two local updates for detecting poisoning attack could be exposed to the server. Aiming at the challenges, in this paper, we propose an Efficient Privacy-preserving and Poisoning attack Resistant scheme for Federated Learning, named EPPRFL, which preserves the privacy for local updates and some intermediate information used to detect poisoning attack. In particular, we design an efficient poisoning attack detection method based on Euclidean distance filtering & clipping technique, named F&C. Then, considering the privacy preservation of the F&C method, we efficiently customize secure comparison, secure median, secure distance computation and secure clipping protocols based on additive secret sharing. Experimental results and theoretical analysis show that compared with existing schemes, EPPRFL can better resist poisoning attack and has lower computational and communication overheads on the client side.

Big data mining-driven disease risk prediction has become one of the important topics in the field of e-healthcare. However, without the security and privacy assurances, disease risk prediction cannot continue to flourish. To address this challenge, in this paper, an efficient and privacy-preserving disease risk prediction scheme for e-healthcare is proposed, hereafter referred to as EPDP. Compared with the up-to-date works, the proposed EPDP comprehensively achieves two phases of disease risk prediction, i.e., disease model training and disease prediction, while ensuring the privacy preservation. Specifically, a super-increasing sequence is combined with a homomorphic cryptographic algorithm to efficiently extract the symptom set of each disease in the phase of disease model training. Bloom filter technique is introduced to compute the prediction result in the phase of disease risk prediction. Besides, extensive performance evaluations demonstrate that our proposed EPDP attains outstanding efficiency advantage over the state-of-the-art in terms of both computational and communication overheads, and hence our EPDP is more suitable for real-time e-healthcare, especially medical emergency.