Biblio

To allow fine-grained access control of sensitive data, researchers have proposed various types of functional encryption schemes, such as identity-based encryption, searchable encryption and attribute-based encryption. We observe that it is difficult to define some complex access policies in certain application scenarios by using these schemes individually. In this paper, we attempt to address this problem by proposing a functional encryption approach named Key-Policy Attribute-Based Encryption with Attribute Extension (KP-ABE-AE). In this approach, we utilize extended attributes to integrate various encryption schemes that support different access policies under a common top-level KP-ABE scheme, thus expanding the scope of access policies that can be defined. Theoretical analysis and experimental studies are conducted to demonstrate the applicability of the proposed KP-ABE-AE. We also present an optimization for a special application of KP-ABE-AE where IPE schemes are integrated with a KP-ABE scheme. The optimization results in an integrated scheme with better efficiency when compared to the existing encryption schemes that support the same scope of access policies.

Current testing for Deep Neural Networks (DNNs) focuses on quantity of test cases but ignores diversity. To the best of our knowledge, DeepXplore is the first white-box framework for Deep Learning testing by triggering differential behaviors between multiple DNNs and increasing neuron coverage to improve diversity. Since it is based on multiple DNNs facing problems that (1) the framework is not friendly to a single DNN, (2) if incorrect predictions made by all DNNs simultaneously, DeepXplore cannot generate test cases. This paper presents Test4Deep, a white-box testing framework based on a single DNN. Test4Deep avoids mistakes of multiple DNNs by inducing inconsistencies between predicted labels of original inputs and that of generated test inputs. Meanwhile, Test4Deep improves neuron coverage to capture more diversity by attempting to activate more inactivated neurons. The proposed method was evaluated on three popular datasets with nine DNNs. Compared to DeepXplore, Test4Deep produced average 4.59% (maximum 10.49%) more test cases that all found errors and faults of DNNs. These test cases got 19.57% more diversity increment and 25.88% increment of neuron coverage. Test4Deep can further be used to improve the accuracy of DNNs by average up to 5.72% (maximum 7.0%).

Recently, many methods have been proposed to prevent privacy leakage in record linkage by encoding record pair data into another anonymous space. Nevertheless, they cannot perform well in some circumstances due to high computational complexities, low privacy guarantees or loss of data utility. In this paper, we propose distance-aware encoding mechanisms to compare numerical values in the anonymous space. We first embed numerical values into Hamming space by a low-computational encoding algorithm with randomized bit vector. To provide rigorous privacy guarantees, we use the random response based on differential privacy to keep global indistinguishability of original data and use Laplace noises via pufferfish mechanism to provide local indistinguishability. Besides, we provide an approach for embedding and privacy-related parameters selection to improve data utility. Experiments on datasets from different data distributions and application contexts validate that our approaches can be used efficiently in privacy-preserving record linkage tasks compared with previous works and have excellent performance even under very small privacy budgets.