1,793 research outputs found
Practical and Secure Outsourcing Algorithms of Matrix Operations Based on a Novel Matrix Encryption Method
With the recent growth and commercialization of cloud computing, outsourcing computation has become one of the most important cloud services, which allows the resource-constrained clients to efficiently perform large-scale computation in a pay-per-use manner. Meanwhile, outsourcing large scale computing problems and computationally intensive applications to the cloud has become prevalent in the science and engineering computing community. As important fundamental operations, large-scale matrix multiplication computation (MMC), matrix inversion computation (MIC), and matrix determinant computation (MDC) have been frequently used. In this paper, we present three new algorithms to enable secure, verifiable, and efficient outsourcing of MMC, MIC, and MDC operations to a cloud that may be potentially malicious. The main idea behind our algorithms is a novel matrix encryption/decryption method utilizing consecutive and sparse unimodular matrix transformations. Compared to previous works, this versatile technique can be applied to many matrix operations while achieving a good balance between security and efficiency. First, the proposed algorithms provide robust confidentiality by concealing the local information of the entries in the input matrices. Besides, they also protect the statistic information of the original matrix. Moreover, these algorithms are highly efficient. Our theoretical analysis indicates that the proposed algorithms reduce the time overhead on the client side from O(n 2.3728639 ) to O(n 2 ). Finally, the extensive experimental evaluations demonstrate the practical efficiency and effectiveness of our algorithms
Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure
function evaluation (SFE) which enables two parties to jointly compute a
function without disclosing their private inputs. Chameleon combines the best
aspects of generic SFE protocols with the ones that are based upon additive
secret sharing. In particular, the framework performs linear operations in the
ring using additively secret shared values and nonlinear
operations using Yao's Garbled Circuits or the Goldreich-Micali-Wigderson
protocol. Chameleon departs from the common assumption of additive or linear
secret sharing models where three or more parties need to communicate in the
online phase: the framework allows two parties with private inputs to
communicate in the online phase under the assumption of a third node generating
correlated randomness in an offline phase. Almost all of the heavy
cryptographic operations are precomputed in an offline phase which
substantially reduces the communication overhead. Chameleon is both scalable
and significantly more efficient than the ABY framework (NDSS'15) it is based
on. Our framework supports signed fixed-point numbers. In particular,
Chameleon's vector dot product of signed fixed-point numbers improves the
efficiency of mining and classification of encrypted data for algorithms based
upon heavy matrix multiplications. Our evaluation of Chameleon on a 5 layer
convolutional deep neural network shows 133x and 4.2x faster executions than
Microsoft CryptoNets (ICML'16) and MiniONN (CCS'17), respectively
A practical and secure multi-keyword search method over encrypted cloud data
Cloud computing technologies become more and more popular every year, as many organizations tend to outsource their data utilizing robust and fast services of clouds while lowering the cost of hardware ownership. Although its benefits are welcomed, privacy is still a remaining concern that needs to be addressed. We propose an efficient privacy-preserving search method over encrypted cloud data that utilizes minhash functions. Most of the work in literature can only support a single feature search in queries which reduces the effectiveness. One of the main advantages of our proposed method is the capability of multi-keyword search in a single query. The proposed method is proved to satisfy adaptive semantic security definition. We also combine an effective ranking capability that is based on term frequency-inverse document frequency (tf-idf) values of keyword document pairs. Our analysis demonstrates that the proposed scheme is proved to be privacy-preserving, efficient and effective
Confidential Machine Learning on Untrusted Platforms: a Survey
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable processing or collaborative learning. Thus, sensitive data and models are in danger of unauthorized access, misuse, and privacy compromises. A relatively new body of research confidentially trains machine learning models on protected data to address these concerns. In this survey, we summarize notable studies in this emerging area of research. With a unified framework, we highlight the critical challenges and innovations in outsourcing machine learning confidentially. We focus on the cryptographic approaches for confidential machine learning (CML), primarily on model training, while also covering other directions such as perturbation-based approaches and CML in the hardware-assisted computing environment. The discussion will take a holistic way to consider a rich context of the related threat models, security assumptions, design principles, and associated trade-offs amongst data utility, cost, and confidentiality
e-SAFE: Secure, Efficient and Forensics-Enabled Access to Implantable Medical Devices
To facilitate monitoring and management, modern Implantable Medical Devices
(IMDs) are often equipped with wireless capabilities, which raise the risk of
malicious access to IMDs. Although schemes are proposed to secure the IMD
access, some issues are still open. First, pre-sharing a long-term key between
a patient's IMD and a doctor's programmer is vulnerable since once the doctor's
programmer is compromised, all of her patients suffer; establishing a temporary
key by leveraging proximity gets rid of pre-shared keys, but as the approach
lacks real authentication, it can be exploited by nearby adversaries or through
man-in-the-middle attacks. Second, while prolonging the lifetime of IMDs is one
of the most important design goals, few schemes explore to lower the
communication and computation overhead all at once. Finally, how to safely
record the commands issued by doctors for the purpose of forensics, which can
be the last measure to protect the patients' rights, is commonly omitted in the
existing literature. Motivated by these important yet open problems, we propose
an innovative scheme e-SAFE, which significantly improves security and safety,
reduces the communication overhead and enables IMD-access forensics. We present
a novel lightweight compressive sensing based encryption algorithm to encrypt
and compress the IMD data simultaneously, reducing the data transmission
overhead by over 50% while ensuring high data confidentiality and usability.
Furthermore, we provide a suite of protocols regarding device pairing,
dual-factor authentication, and accountability-enabled access. The security
analysis and performance evaluation show the validity and efficiency of the
proposed scheme
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