Secure Outsourced Computation on Encrypted Data

Homomorphic encryption (HE) is a promising cryptographic technique that supports computations on encrypted data without requiring decryption first. This ability allows sensitive data, such as genomic, financial, or location data, to be outsourced for evaluation in a resourceful third-party such as the cloud without compromising data privacy. Basic homomorphic primitives support addition and multiplication on ciphertexts. These primitives can be utilized to represent essential computations, such as logic gates, which subsequently can support more complex functions. We propose the construction of efficient cryptographic protocols as building blocks (e.g., equality, comparison, and counting) that are commonly used in data analytics and machine learning. We explore the use of these building blocks in two privacy-preserving applications. One application leverages our secure prefix matching algorithm, which builds on top of the equality operation, to process geospatial queries on encrypted locations. The other applies our secure comparison protocol to perform conditional branching in private evaluation of decision trees. There are many outsourced computations that require joint evaluation on private data owned by multiple parties. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of the recent advances of genome sequencing technology. Due to the sensitivity of genomic data, this data is encrypted using different keys possessed by different data owners. Computing on ciphertexts encrypted with multiple keys is a non-trivial task. Current solutions often require a joint key setup before any computation such as in threshold HE or incur large ciphertext size (at best, grows linearly in the number of involved keys) such as in multi-key HE. We propose a hybrid approach that combines the advantages of threshold and multi-key HE to support computations on ciphertexts encrypted with different keys while vastly reducing ciphertext size. Moreover, we propose the SparkFHE framework to support large-scale secure data analytics in the Cloud. SparkFHE integrates Apache Spark with Fully HE to support secure distributed data analytics and machine learning and make two novel contributions: (1) enabling Spark to perform efficient computation on large datasets while preserving user privacy, and (2) accelerating intensive homomorphic computation through parallelization of tasks across clusters of computing nodes. To our best knowledge, SparkFHE is the first addressing these two needs simultaneously

Data Auditing and Security in Cloud Computing: Issues, Challenges and Future Directions

Cloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discussed

Data auditing and security in cloud computing: issues, challenges and future directions

Cloud computing is one of the significant development that utilizes progressive computational power and upgrades data distribution and data storing facilities. With cloud information services, it is essential for information to be saved in the cloud and also distributed across numerous customers. Cloud information repository is involved with issues of information integrity, data security and information access by unapproved users. Hence, an autonomous reviewing and auditing facility is necessary to guarantee that the information is effectively accommodated and used in the cloud. In this paper, a comprehensive survey on the state-of-art techniques in data auditing and security are discussed. Challenging problems in information repository auditing and security are presented. Finally, directions for future research in data auditing and security have been discusse

Privacy Preserving Deep Neural Network Prediction using Trusted Hardware

In recent years machine learning has gained a lot of attention not only in the scientific community but also in user-facing applications. Today, many applications utilise machine learning to take advantage of its capabilities. With such applications, users actively or passively input data that is used by state-of-the-art algorithms to generate accurate predictions. Due to the extensive work necessary to fine-tune these algorithms for a specific task, they are predominantly executed in the cloud where they can be protected from competitors or malicious users. As a result, users' privacy might be at risk as their data is sent to and processed by remote cloud services. Depending on the application, users might expose highly sensitive data, meaning a malicious provider could harvest extensive amounts of personal data from its users. In order to protect user privacy without compromising the confidentiality guarantees of traditional solutions, we propose using trusted hardware for privacy preserving deep neural network predictions. Our solution consists of a hardware-backed prediction service and a client device that connects to said service. All machine learning computations executed by the prediction service that depend on input data are protected by a trusted hardware component, called a Trusted Execution Environment. This can be verified by users via remote attestation to ensure their data remains protected. In addition, we have built a proof-of-concept implementation of our solution using Intel Software Guard Extensions (SGX). Compared to existing solutions relying on homomorphic encryption, our proof-of-concept implementation vastly increases the set of supported machine learning algorithms. Moreover, our implementation is tightly integrated into the existing pipeline of machine learning tools by supporting the Open Neural Network Exchange (ONNX) Format. Furthermore, we focus on minimising our Trusted Computing Base (TCB), thus our proof-of-concept implementation only consists of 4,500 lines of code. Additionally, we achieve a 7x increase in throughput whilst decreasing the latency 40x compared to prior work. In our tests, SGX reduced throughput by 11% and increased latency by 21% compared to our baseline implementation without SGX

Efficient Private Information Retrieval

A vast amount of today\u27s Internet users\u27 on line activities consists of queries to various types of databases. From traditional search engines to modern cloud based services, a person\u27s everyday queries over a period of time on various data sources, will leave a trail visible to the query processor, which can reveal significant and possibly sensitive information about her. Private Information Retrieval (PIR) algorithms can be leveraged for providing perfect privacy to users\u27 queries, though at a restrictive computational cost. In this work, we consider today\u27s highly distributed computing environments, as well as certain secure-hardware devices, for optimizing existing PIR solutions. In particular, we initially employ available secure-hardware in a novel approach with the goal of providing faster and constant private query responses, by sacrificing some degree of privacy. Further on, we utilize the widely used Message Passing Interface (MPI) protocol for designing a library which can be used in third party software for performing private queries

2022, nr 2, JTIT

kwartalni

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method