1,704 research outputs found
Privacy-Aware Processing of Biometric Templates by Means of Secure Two-Party Computation
The use of biometric data for person identification and access control is gaining more and more popularity. Handling biometric data, however, requires particular care, since biometric data is indissolubly tied to the identity of the owner hence raising important security and privacy issues. This chapter focuses on the latter, presenting an innovative approach that, by relying on tools borrowed from Secure Two Party Computation (STPC) theory, permits to process the biometric data in encrypted form, thus eliminating any risk that private biometric information is leaked during an identification process. The basic concepts behind STPC are reviewed together with the basic cryptographic primitives needed to achieve privacy-aware processing of biometric data in a STPC context. The two main approaches proposed so far, namely homomorphic encryption and garbled circuits, are discussed and the way such techniques can be used to develop a full biometric matching protocol described. Some general guidelines to be used in the design of a privacy-aware biometric system are given, so as to allow the reader to choose the most appropriate tools depending on the application at hand
Study on A Proposed Scheme for Generating Inverted Encryption Index Structure Based on Public Homomorphic Encryption
This research article focuses on the formidable challenge of efficiently searching through encrypted data in cloud environments, particularly as an extended number of users adopt encryption for their sensitive Information. The inverted index has proven to be a robust and effective searchable index structure in this context. However, striking a balance between preserving user privacy and enabling conjunctive multi-keyword searches remains a significant hurdle for existing solutions. In response to this challenge, the authors propose an innovative public-key-based encrypted file system. This system follows conjunctive multi-keyword searches but also eliminates the restrictive one-time-only searching limitation that has been a drawback in previous approaches. The proposed solution goes beyond conventional methods by safeguarding the search pattern, a critical aspect of user privacy. Their approach involves the integration of a probabilistic trapdoor- generating mechanism, adding an extra layer of security. To fortify their technique and adhere to more stringent security standards, the authors introduce an oblivious transmission control mechanism. This mechanism enhances the overall security posture of the system, ensuring robust protection against potential threats. The simulation results presented in the article demonstrate the practical proposed technique in real-world applications. Despite the additional security measures, the approach incurs reasonable overhead, making it a viable and efficient solution for cloud-based encrypted data searches
MPC for MPC: Secure Computation on a Massively Parallel Computing Architecture
Massively Parallel Computation (MPC) is a model of computation widely believed to best capture realistic parallel computing architectures such as large-scale MapReduce and Hadoop clusters. Motivated by the fact that many data analytics tasks performed on these platforms involve sensitive user data, we initiate the theoretical exploration of how to leverage MPC architectures to enable efficient, privacy-preserving computation over massive data. Clearly if a computation task does not lend itself to an efficient implementation on MPC even without security, then we cannot hope to compute it efficiently on MPC with security. We show, on the other hand, that any task that can be efficiently computed on MPC can also be securely computed with comparable efficiency. Specifically, we show the following results:
- any MPC algorithm can be compiled to a communication-oblivious counterpart while asymptotically preserving its round and space complexity, where communication-obliviousness ensures that any network intermediary observing the communication patterns learn no information about the secret inputs;
- assuming the existence of Fully Homomorphic Encryption with a suitable notion of compactness and other standard cryptographic assumptions, any MPC algorithm can be compiled to a secure counterpart that defends against an adversary who controls not only intermediate network routers but additionally up to 1/3 - ? fraction of machines (for an arbitrarily small constant ?) - moreover, this compilation preserves the round complexity tightly, and preserves the space complexity upto a multiplicative security parameter related blowup.
As an initial exploration of this important direction, our work suggests new definitions and proposes novel protocols that blend algorithmic and cryptographic techniques
A HYBRIDIZED ENCRYPTION SCHEME BASED ON ELLIPTIC CURVE CRYPTOGRAPHY FOR SECURING DATA IN SMART HEALTHCARE
Recent developments in smart healthcare have brought us a great deal of convenience. Connecting common objects to the Internet is made possible by the Internet of Things (IoT). These connected gadgets have sensors and actuators for data collection and transfer. However, if users' private health information is compromised or exposed, it will seriously harm their privacy and may endanger their lives. In order to encrypt data and establish perfectly alright access control for such sensitive information, attribute-based encryption (ABE) has typically been used. Traditional ABE, however, has a high processing overhead. As a result, an effective security system algorithm based on ABE and Fully Homomorphic Encryption (FHE) is developed to protect health-related data. ABE is a workable option for one-to-many communication and perfectly alright access management of encrypting data in a cloud environment. Without needing to decode the encrypted data, cloud servers can use the FHE algorithm to take valid actions on it. Because of its potential to provide excellent security with a tiny key size, elliptic curve cryptography (ECC) algorithm is also used. As a result, when compared to related existing methods in the literature, the suggested hybridized algorithm (ABE-FHE-ECC) has reduced computation and storage overheads. A comprehensive safety evidence clearly shows that the suggested method is protected by the Decisional Bilinear Diffie-Hellman postulate. The experimental results demonstrate that this system is more effective for devices with limited resources than the conventional ABE when the system’s performance is assessed by utilizing standard model
SoK: Fully Homomorphic Encryption Accelerators
Fully Homomorphic Encryption~(FHE) is a key technology enabling
privacy-preserving computing. However, the fundamental challenge of FHE is its
inefficiency, due primarily to the underlying polynomial computations with high
computation complexity and extremely time-consuming ciphertext maintenance
operations. To tackle this challenge, various FHE accelerators have recently
been proposed by both research and industrial communities. This paper takes the
first initiative to conduct a systematic study on the 14 FHE accelerators --
cuHE/cuFHE, nuFHE, HEAT, HEAX, HEXL, HEXL-FPGA, 100, F1, CraterLake,
BTS, ARK, Poseidon, FAB and TensorFHE. We first make our observations on the
evolution trajectory of these existing FHE accelerators to establish a
qualitative connection between them. Then, we perform testbed evaluations of
representative open-source FHE accelerators to provide a quantitative
comparison on them. Finally, with the insights learned from both qualitative
and quantitative studies, we discuss potential directions to inform the future
design and implementation for FHE accelerators
The Quantum Frontier
The success of the abstract model of computation, in terms of bits, logical
operations, programming language constructs, and the like, makes it easy to
forget that computation is a physical process. Our cherished notions of
computation and information are grounded in classical mechanics, but the
physics underlying our world is quantum. In the early 80s researchers began to
ask how computation would change if we adopted a quantum mechanical, instead of
a classical mechanical, view of computation. Slowly, a new picture of
computation arose, one that gave rise to a variety of faster algorithms, novel
cryptographic mechanisms, and alternative methods of communication. Small
quantum information processing devices have been built, and efforts are
underway to build larger ones. Even apart from the existence of these devices,
the quantum view on information processing has provided significant insight
into the nature of computation and information, and a deeper understanding of
the physics of our universe and its connections with computation.
We start by describing aspects of quantum mechanics that are at the heart of
a quantum view of information processing. We give our own idiosyncratic view of
a number of these topics in the hopes of correcting common misconceptions and
highlighting aspects that are often overlooked. A number of the phenomena
described were initially viewed as oddities of quantum mechanics. It was
quantum information processing, first quantum cryptography and then, more
dramatically, quantum computing, that turned the tables and showed that these
oddities could be put to practical effect. It is these application we describe
next. We conclude with a section describing some of the many questions left for
future work, especially the mysteries surrounding where the power of quantum
information ultimately comes from.Comment: Invited book chapter for Computation for Humanity - Information
Technology to Advance Society to be published by CRC Press. Concepts
clarified and style made more uniform in version 2. Many thanks to the
referees for their suggestions for improvement
Secure and Privacy-Preserving Data Aggregation Protocols for Wireless Sensor Networks
This chapter discusses the need of security and privacy protection mechanisms
in aggregation protocols used in wireless sensor networks (WSN). It presents a
comprehensive state of the art discussion on the various privacy protection
mechanisms used in WSNs and particularly focuses on the CPDA protocols proposed
by He et al. (INFOCOM 2007). It identifies a security vulnerability in the CPDA
protocol and proposes a mechanism to plug that vulnerability. To demonstrate
the need of security in aggregation process, the chapter further presents
various threats in WSN aggregation mechanisms. A large number of existing
protocols for secure aggregation in WSN are discussed briefly and a protocol is
proposed for secure aggregation which can detect false data injected by
malicious nodes in a WSN. The performance of the protocol is also presented.
The chapter concludes while highlighting some future directions of research in
secure data aggregation in WSNs.Comment: 32 pages, 7 figures, 3 table
SoK: Privacy Preserving Machine Learning using Functional Encryption: Opportunities and Challenges
With the advent of functional encryption, new possibilities for computation
on encrypted data have arisen. Functional Encryption enables data owners to
grant third-party access to perform specified computations without disclosing
their inputs. It also provides computation results in plain, unlike Fully
Homomorphic Encryption. The ubiquitousness of machine learning has led to the
collection of massive private data in the cloud computing environment. This
raises potential privacy issues and the need for more private and secure
computing solutions. Numerous efforts have been made in privacy-preserving
machine learning (PPML) to address security and privacy concerns. There are
approaches based on fully homomorphic encryption (FHE), secure multiparty
computation (SMC), and, more recently, functional encryption (FE). However,
FE-based PPML is still in its infancy and has not yet gotten much attention
compared to FHE-based PPML approaches. In this paper, we provide a
systematization of PPML works based on FE summarizing state-of-the-art in the
literature. We focus on Inner-product-FE and Quadratic-FE-based machine
learning models for the PPML applications. We analyze the performance and
usability of the available FE libraries and their applications to PPML. We also
discuss potential directions for FE-based PPML approaches. To the best of our
knowledge, this is the first work to systematize FE-based PPML approaches
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