Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

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The knowledge complexity of interactive proof-systems

An efficient certificateless public key encryption scheme with authorized equality test in healthcare environments

Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.,The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).,The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.,The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

Emphasizing privacy and security of edge intelligence with machine learning for healthcare

An application of metadata-based image retrieval system for facility management

Protocols for secure multiparty computation (MPC) enable a set of mutually distrusting parties to compute an arbitrary function of their inputs while preserving basic security properties like privacy and correctness. The study of MPC was initiated in the 1980s where it was shown that any function can be securely computed, thus demonstrating the power of this notion. However, these proofs of feasibility were theoretical in nature and it is only recently that MPC protocols started to become efficient enough for use in practice. Today, we have protocols that can carry out large and complex computations in very reasonable time (and can even be very fast, depending on the computation and the setting). Despite this amazing progress, there is still a major obstacle to the adoption and use of MPC due to the huge expertise needed to design a specific MPC execution. In particular, the function to be computed needs to be represented as an appropriate Boolean or arithmetic circuit, and this requires very specific expertise. In order to overcome this, there has been considerable work on compilation of code to (typically) Boolean circuits. One work in this direction takes a different approach, and this is the SPDZ compiler (not to be confused with the SPDZ protocol) that takes high-level Python code and provides an MPC run-time environment for securely executing that code. The SPDZ compiler can deal with arithmetic and non-arithmetic operations and is extremely powerful. However, until now, the SPDZ compiler could only be used for the specific SPDZ family of protocols, making its general applicability and usefulness very limited. In this paper, we extend the SPDZ compiler so that it can work with general underlying protocols. Our SPDZ extensions were made in mind to enable the use of SPDZ for arbitrary protocols and to make it easy for others to integrate existing and new protocols. We integrated three different types of protocols, an honest-majority protocol for computing arithmetic circuits over a field (for any number of parties), a three-party honest majority protocol for computing arithmetic circuits over the ring of integers Z2n, and the multiparty BMR protocol for computing Boolean circuits. We show that a single high-level SPDZ-Python program can be executed using all of these underlying protocols (as well as the original SPDZ protocol), thereby making SPDZ a true general run-time MPC environment.In order to be able to handle both arithmetic and non-arithmetic operations, the SPDZ compiler relies on conversions from field elements to bits and back. However, these conversions do not apply to ring elements (in particular, they require element division), and we therefore introduce new bit decomposition and recomposition protocols for the ring over integers with replicated secret sharing. These conversions are of independent interest and utilize the structure of Z2n (which is much more amenable to bit decomposition than prime-order fields), and are thus much more efficient than all previous methods. We demonstrate our compiler extensions by running a complex SQL query and a decision tree evaluation over all protocols.

/pdf/generalizing-the-spdz-compiler-for-other-protocols-348k5zf4zw.pdf

Generalizing the SPDZ Compiler For Other Protocols.

With the tremendous growth of smart mobile devices, the Content-Based Image Retrieval (CBIR) becomes popular and has great market potentials. Secure image retrieval has attracted considerable interests recently due to users' security concerns. However, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as data encryption, feature extraction, and image similarity scoring. In this paper, we propose and implement an IND-CPA secure CBIR framework that performs image retrieval on the cloud without the user's constant interaction. A pre-trained deep CNN model, i.e., VGG-16, is used to extract the deep features of an image. The information about the neural network is strictly concealed by utilizing the lattice-based homomorphic scheme. We implement a real number computation mechanism and a divide-and-conquer CNN evaluation protocol to enable our framework to securely and efficiently evaluate the deep CNN with a large number of inputs. We further propose a secure image similarity scoring protocol, which enables the cloud servers to compare two images without knowing any information about their deep features. The comprehensive experimental results show that our framework is efficient and accurate.

/pdf/intelligent-and-secure-content-based-image-retrieval-for-4ofqn6w3h0.pdf

Intelligent and Secure Content-Based Image Retrieval for Mobile Users

With the increasing popularity of mobile cloud computing, image outsourcing provides a cost-effective way to support images management for smart mobile devices. However, users default that the cloud server is not trustworthy, secure image retrieval has attracted considerable interests recently to address users’ privacy concerns. Due to the limited resources such as computing power, battery lifetime, and storage capacity, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as feature extraction and index building. In this paper, we propose a framework that supports cloud server side local-feature (scale-invariant feature transform, SIFT) extraction, index building, and image similarity scoring. We further propose a multi-index for SIFT descriptors, a secure bucket identifier computation protocol, and a secure image similarity computation protocol. In our framework, mobile users only need to encrypt and upload their images to the cloud. The later provides privacy-preserving image retrieval services without the mobile users extra interaction. Our framework greatly reduces computation overhead on the mobile user side. The security analysis and experimental evaluations demonstrate the efficiency and the scalability of our framework.

Privacy-preserving content-based image retrieval for mobile computing

Secure content based image retrieval for mobile users with deep neural networks in the cloud

The Internet-of-Things (IoT) are used everywhere in our daily lives. IoT applications provide us with many useful functionalities such as preventing fires, detecting and tracking objects, controlling and reporting the changes in/outside the environments, and capturing images/videos in our homes, roads, and offices. For example, the images data gathered through the smart sensors of autonomous vehicles can serve in various applications such as traffic monitoring, prediction of road conditions, and classification of objects. Image classification with deep neural networks (DNNs) on the cloud is such a machine learning task and has great market potentials for IoT applications. Nevertheless, the deployment of these “smart” IoT devices and applications can raise the risks of security issues. It still suffers from the challenges of relieving IoT devices from excessive computation burdens, such as data encryption, feature extraction, and image classification. In this paper, we propose and implement an indistinguishability-chosen plaintext attack secure image classification framework with DNN for IoT Applications. The framework performs a secure image classification on the cloud without the IoT device’s constant interaction. We propose and implement a real number computation mechanism and a divide-and-conquer mechanism for the secure evaluation of linear functions in DNNs, as well as a set of unified ideal protocols for the evaluation of non-linear functions in DNNs. The information about the image contents, the private DNNs model parameters and the intermediate results is strictly concealed by the conjunctive use of the lattice-based homomorphic scheme and 2-PC secure computation techniques. A pre-trained deep convolutional neural network model, i.e., Visual Geometry Group (VGG-16), is used to extract the deep features of an image. The comprehensive experimental results show that our framework is efficient and accurate. In addition, we evaluate the security of our framework by performing the white-box membership inference attack which is believed to be the most powerful attack on DNNs models. The failure of the attack indicates that our framework is practical secure.

Secure image classification with deep neural networks for IoT applications

The open data initiative allows the organizations from different countries to share and integrate their data to produce value added information products and services. However, the trust relationship between any two organizations can hardly be established due to the data sensitivity and their specific responsibilities. In this paper, we propose a secure string pattern query processing framework for multiple data owners. With this framework, we design an efficient and secure indexing structure called S ecure S tring PA ttern S earch tree (S2PAStree). S2PAStree allows the execution of the secure string pattern query with logarithmic time complexity. We further propose a set of secure index construction protocols under the scenario that multiple data owners share and integrate their sensitive data. Finally, we conduct comprehensive experiments on three public datasets to evaluate the efficiency and the scalability of our solutions.

Fei Liu

Papers

Intelligent and Secure Content-Based Image Retrieval for Mobile Users

Privacy-preserving content-based image retrieval for mobile computing

Secure content based image retrieval for mobile users with deep neural networks in the cloud

Secure image classification with deep neural networks for IoT applications

Secure string pattern query for open data initiative