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Register a new userComputing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: An Extended Survey
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Added by
Asma Aloufi
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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New cryptographic techniques such as homomorphic encryption (HE) allow computations to be outsourced to and evaluated blindfolded in a resourceful cloud. These computations often require private data owned by multiple participants, engaging in joint evaluation of some functions. For example, Genome-Wide Association Study (GWAS) is becoming feasible because of recent proliferation of genome sequencing technology. Due to the sensitivity of genomic data, these data should be encrypted using different keys. However, supporting computation on ciphertexts encrypted under multiple keys is a non-trivial task. In this paper, we present a comprehensive survey on different state-of-the-art cryptographic techniques and schemes that are commonly used. We review techniques and schemes including Attribute-Based Encryption (ABE), Proxy Re-Encryption (PRE), Threshold Homomorphic Encryption (ThHE), and Multi-Key Homomorphic Encryption (MKHE). We analyze them based on different system and security models, and examine their complexities. We share lessons learned and draw observations for designing better schemes with reduced overheads.
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Remote monitoring to support aging in place is an active area of research. Advanced computer vision technology based on deep learning can provide near real-time home monitoring to detect falling and symptoms related to seizure, and stroke. Affordable webcams, together with cloud computing services (to run machine learning algorithms), can potentially bring significant social and health benefits. However, it has not been deployed in practice because of privacy and security concerns. People may feel uncomfortable sending their videos of daily activities (with potentially sensitive private information) to a computing service provider (e.g., on a commercial cloud). In this paper, we propose a novel strategy to resolve this dilemma by applying fully homomorphic encryption (FHE) to an alternative representation of human actions (i.e., skeleton joints), which guarantees information confidentiality while retaining high-performance action detection at a low cost. We design an FHE-friendly neural network for action recognition and present a secure neural network evaluation strategy to achieve near real-time action detection. Our framework for private inference achieves an 87.99% recognition accuracy (86.21% sensitivity and 99.14% specificity in detecting falls) with a latency of 3.1 seconds on real-world datasets. Our evaluation shows that our elaborated and fine-tuned method reduces the inference latency by 23.81%~74.67% over a straightforward implementation.
Fully homomorphic encryption (FHE) enables a simple, attractive framework for secure search. Compared to other secure search systems, no costly setup procedure is necessary; it is sufficient for the client merely to upload the encrypted database to the server. Confidentiality is provided because the server works only on the encrypted query and records. While the search functionality is enabled by the full homomorphism of the encryption scheme. For this reason, researchers have been paying increasing attention to this problem. Since Akavia et al. (CCS 2018) presented a framework for secure search on FHE encrypted data and gave a working implementation called SPiRiT, several more efficient realizations have been proposed. In this paper, we identify the main bottlenecks of this framework and show how to significantly improve the performance of FHE-base secure search. In particular, 1. To retrieve $ell$ matching items, the existing framework needs to repeat the protocol $ell$ times sequentially. In our new framework, all matching items are retrieved in parallel in a single protocol execution. 2. The most recent work by Wren et al. (CCS 2020) requires $O(n)$ multiplications to compute the first matching index. Our solution requires no homomorphic multiplication, instead using only additions and scalar multiplications to encode all matching indices. 3. Our implementation and experiments show that to fetch 16 matching records, our system gives an 1800X speed-up over the state of the art in fetching the query results resulting in a 26X speed-up for the full search functionality.
Cloud service providers offer a low-cost and convenient solution to host unstructured data. However, cloud services act as third-party solutions and do not provide control of the data to users. This has raised security and privacy concerns for many organizations (users) with sensitive data to utilize cloud-based solutions. User-side encryption can potentially address these concerns by establishing user-centric cloud services and granting data control to the user. Nonetheless, user-side encryption limits the ability to process (e.g., search) encrypted data on the cloud. Accordingly, in this research, we provide a framework that enables processing (in particular, searching) of encrypted multi-organizational (i.e., multi-source) big data without revealing the data to cloud provider. Our framework leverages locality feature of edge computing to offer a user-centric search ability in a real-time manner. In particular, the edge system intelligently predicts the users search pattern and prunes the multi-source big data search space to reduce the search time. The pruning system is based on efficient sampling from the clustered big dataset on the cloud. For each cluster, the pruning system dynamically samples appropriate number of terms based on the users search tendency, so that the cluster is optimally represented. We developed a prototype of a user-centric search system and evaluated it against multiple datasets. Experimental results demonstrate 27% improvement in the pruning quality and search accuracy.
Spatial queries like range queries, nearest neighbor, circular range queries etc. are the most widely used queries in the location-based applications. Building secure and efficient solutions for these queries in the cloud computing framework is critical and has been an area of active research. This paper focuses on the problem of Secure Circular Range Queries (SCRQ), where client submits an encrypted query (consisting of a center point and radius of the circle) and the cloud (storing encrypted data points) has to return the points lying inside the circle. The existing solutions for this problem suffer from various disadvantages such as high processing time which is proportional to square of the query radius, query generation phase which is directly proportional to the number of points covered by the query etc. This paper presents solution for the above problem which is much more efficient than the existing solutions. Three protocols are proposed with varying characteristics. It is shown that all the three protocols are secure. The proposed protocols can be extended to multiple dimensions and thus are able to handle Secure Hypersphere Range Queries (SHRQ) as well. Internally the proposed protocols use pairing-based cryptography and a concept of lookup table. To enable the efficient use of limited size lookup table, a new storage scheme is presented. The proposed storage scheme enables the protocols to handle query with much larger radius values. Using the SHRQ protocols, we also propose a mechanism to answer the Secure range Queries. Extensive performance evaluation has been done to evaluate the efficiency of the proposed protocols
Fully Homomorphic Encryption (FHE) is a relatively recent advancement in the field of privacy-preserving technologies. FHE allows for the arbitrary depth computation of both addition and multiplication, and thus the application of abelian/polynomial equations, like those found in deep learning algorithms. This project investigates, derives, and proves how FHE with deep learning can be used at scale, with relatively low time complexity, the problems that such a system incurs, and mitigations/solutions for such problems. In addition, we discuss how this could have an impact on the future of data privacy and how it can enable data sharing across various actors in the agri-food supply chain, hence allowing the development of machine learning-based systems. Finally, we find that although FHE incurs a high spatial complexity cost, the time complexity is within expected reasonable bounds, while allowing for absolutely private predictions to be made, in our case for milk yield prediction.
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