Secure multi-party computation (MPC) allows a set of parties to compute a function jointly while keeping their inputs private. Compared with the MPC based on garbled circuits,some recent research results show that MPC based on secret sharing (SS) works at a very high speed. Moreover, SS-based MPC can be easily vectorized and achieve higher throughput. In SS-based MPC, however, we need many communication rounds for computing concrete protocols like equality check, less-than comparison, etc. This property is not suited for large-latency environments like the Internet (or WAN). In this paper, we construct semi-honest secure communication-efficient two-party protocols. The core technique is Beaver triple extension, which is a new tool for treating multi-fan-in gates, and we also show how to use it efficiently. We mainly focus on reducing the number of communication rounds, and our protocols also succeed in reducing the number of communication bits (in most cases). As an example, we propose a less-than comparison protocol (under practical parameters) with three communication rounds. Moreover, the number of communication bits is also $38.4\%$ fewer. As a result, total online execution time is $56.1\%$ shorter than the previous work adopting the same settings. Although the computation costs of our protocols are more expensive than those of previous work, we confirm via experiments that such a disadvantage has small effects on the whole online performance in the typical WAN environments.
Secure multi-party computation (SMPC) protocols allow several parties that distrust each other to collectively compute a function on their inputs. In this paper, we introduce a protocol that lifts classical SMPC to quantum SMPC in a composably and statistically secure way, even for a single honest party. Unlike previous quantum SMPC protocols, our proposal only requires very limited quantum resources from all but one party; it suffices that the weak parties, i.e. the clients, are able to prepare single-qubit states in the X-Y plane. The novel quantum SMPC protocol is constructed in a naturally…
Privacy and energy are primary concerns for sensor devices that offload compute to a potentially untrusted edge server or cloud. Homomorphic Encryption (HE) enables offload processing of encrypted data. HE offload processing retains data privacy, but is limited by the need for frequent communication between the client device and the offload server. Existing client-aided encrypted computing systems are optimized for performance on the offload server, failing to sufficiently address client costs, and precluding HE offload for low-resource (e.g., IoT) devices. We introduce Client-aided HE for Opa…
With the rapid development of artificial intelligence and the advent of the 5G era, deep learning has received extensive attention from researchers. Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. However, the training process still requires vast computations, and therefore cannot be accomplished by some resource-constrained devices. To solve this problem, the resource-constrained device can outsource the BLS algorithm to cloud servers. Nevertheless, some security challenges…
ADCA: Attention-Driven Multi-Party Collusion Attack in Federated Self-Supervised Learning
School of Information and Artificial Intelligence, Yangzhou University（扬州大学信息科学与人工智能学院）；School of Computing and Artificial Intelligence, Chaohu University（巢湖大学计算与人工智能学院）；Tacoma School of Engineering and Technology, University of Washington（华盛顿大学塔科马工程技术学院）
This paper studies privacy-preserving weighted federated learning within the oracle-aided multi-party computation (MPC) framework. The contribution of this paper mainly comprises the following three-fold: In the first fold, a new notion which we call weighted federated learning (wFL) is introduced and formalized inspired by McMahan et al.'s seminal paper. The weighted federated learning concept formalized in this paper differs from that presented in McMahan et al.'s paper since both addition and multiplication operations are executed over ciphers in our model while these operations are execute…
We define the functionality of delegated pseudo-secret random qubit generator (PSRQG), where a classical client can instruct the preparation of a sequence of random qubits at some distant party. Their classical description is (computationally) unknown to any other party (including the distant party preparing them) but known to the client. We emphasize the unique feature that no quantum communication is required to implement PSRQG. This enables classical clients to perform a class of quantum communication protocols with only a public classical channel with a quantum server. A key such example i…
With real-time certificate validation checking, a public-key-using system that needs to validate a certificate executes a transaction with a specialized validation party. At the end of the transaction the validation party returns an indication about the validity status of the certificate. This paper analysis the public key (PbK) certificate validation service from a practical point of view by describing the implementation of a system that makes use of the Data Validation and Certification Server (DVCS) protocols to provide certificate validation service to the Relying Parties (RPs). However th…
A client wishes to outsource computation on confidential data to a network of parties. He does not trust a single party but believes that multiple parties do not collude. To solve this problem, we use the idea of treating one of the parties as a helper. A helper assists computation only. Often using more parties ensures confidentiality despite more corrupted parties. This does not hold for adding a helper. But a helper can in some cases lower the amount of communication asymptotically to the theoretical minimum of one bit per AND gate, improving significantly on schemes without a helper. It ca…
University of Arizona（亚利桑那大学）；Emory University（埃默里大学）
Federated Learning (FL) allows multiple participating clients to train machine learning models collaboratively by keeping their datasets local and only exchanging model updates. Existing FL protocol designs have been shown to be vulnerable to attacks that aim to compromise data privacy and/or model robustness. Recently proposed defenses focused on ensuring either privacy or robustness, but not both. In this paper, we develop a framework called PRECAD, which simultaneously achieves differential privacy (DP) and enhances robustness against model poisoning attacks with the help of cryptography. U…
When multiple parties that deal with private data aim for a collaborative prediction task such as medical image classification, they are often constrained by data protection regulations and lack of trust among collaborating parties. If done in a privacy-preserving manner, predictive analytics can benefit from the collective prediction capability of multiple parties holding complementary datasets on the same machine learning task. This paper presents PRICURE, a system that combines complementary strengths of secure multi-party computation (SMPC) and differential privacy (DP) to enable privacy-p…
Recently, major progress has been made towards the realisation of quantum internet to enable a broad range of classically intractable applications. These applications such as delegated quantum computation require running a secure identification protocol between a low-resource and a high-resource party to provide secure communication. In this work, we propose two identification protocols based on the emerging hardware secure solutions, the quantum Physical Unclonable Functions (qPUFs). The first protocol allows a low-resource party to prove its identity to a high-resource party and in the secon…
Georgia Institute of Technology（佐治亚理工学院）；J.P. Morgan AI Research（摩根大通人工智能研究院）
Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within its own local systems. This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters. Most federated learning systems therefore use differential privacy to introduce noise to the parameters. This adds uncertainty to any attempt to reveal private client data, but also reduces the accuracy of the shared mod…
ConcurORAM is a parallel, multi-client oblivious RAM (ORAM) that eliminates waiting for concurrent stateless clients and allows overall throughput to scale gracefully, without requiring trusted third party components (proxies) or direct inter-client coordination. A key insight behind ConcurORAM is the fact that, during multi-client data access, only a subset of the concurrently-accessed server-hosted data structures require access privacy guarantees. Everything else can be safely implemented as oblivious data structures that are later synced securely and efficiently during an ORAM "eviction". …
University of British Columbia（不列颠哥伦比亚大学）；University of Toronto（多伦多大学）；University of California, San Diego（加利福尼亚大学圣地亚哥分校）
Distributed machine learning (ML) systems today use an unsophisticated threat model: data sources must trust a central ML process. We propose a brokered learning abstraction that allows data sources to contribute towards a globally-shared model with provable privacy guarantees in an untrusted setting. We realize this abstraction by building on federated learning, the state of the art in multi-party ML, to construct TorMentor: an anonymous hidden service that supports private multi-party ML. We define a new threat model by characterizing, developing and evaluating new attacks in the brokered le…
We investigate the problem of multi-party private set intersection (MP-PSI). In MP-PSI, there are $M$ parties, each storing a data set $\mathcal{p}_i$ over $N_i$ replicated and non-colluding databases, and we want to calculate the intersection of the data sets $\cap_{i=1}^M \mathcal{p}_i$ without leaking any information beyond the set intersection to any of the parties. We consider a specific communication protocol where one of the parties, called the leader party, initiates the MP-PSI protocol by sending queries to the remaining parties which are called client parties. The client parties are …
Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol. However, this process assumes a trusted centralized infrastructure for coordination, and clients must trust that the central service does not use the byproducts of client data. In addition to this, a group of malicious clients could also harm the performance of the model by carrying out a poisoning attack. As a response, we propose Biscotti: a fully decentralized peer to peer (P…
In distributed differential privacy, the parties perform analysis over their joint data while preserving the privacy for both datasets. Interestingly, for a few fundamental two-party functions such as inner product and Hamming distance, the accuracy of the distributed solution lags way behind what is achievable in the client-server setting. McGregor, Mironov, Pitassi, Reingold, Talwar, and Vadhan [FOCS '10] proved that this gap is inherent, showing upper bounds on the accuracy of (any) distributed solution for these functions. These limitations can be bypassed when settling for computational d…
Efficient user revocation is a necessary but challenging problem in many multi-user cryptosystems. Among known approaches, server-aided revocation yields a promising solution, because it allows to outsource the major workloads of system users to a computationally powerful third party, called the server, whose only requirement is to carry out the computations correctly. Such a revocation mechanism was considered in the settings of identity-based encryption and attribute-based encryption by Qin et al. (ESORICS 2015) and Cui et al. (ESORICS 2016), respectively. In this work, we consider the serve…
Cloud computing systems, in which clients rent and share computing resources of third party platforms, have gained widespread use in recent years. Furthermore, cloud computing for mobile systems (i.e., systems in which the clients are mobile devices) have too been receiving considerable attention in technical literature. We propose a new method of delegating computations of resource-constrained mobile clients, in which multiple servers interact to construct an encrypted program known as garbled circuit. Next, using garbled inputs from a mobile client, another server executes this garbled circu…
This paper proposes Impala, a new cryptographic protocol for private inference in the client-cloud setting. Impala builds upon recent solutions that combine the complementary strengths of homomorphic encryption (HE) and secure multi-party computation (MPC). A series of protocol optimizations are developed to reduce both communication and performance bottlenecks. First, we remove MPC's overwhelmingly high communication cost from the client by introducing a proxy server and developing a low-overhead key switching technique. Key switching reduces the clients bandwidth by multiple orders of magnit…
Recent developments in blockchains and edge computing allows to deploy decentralized shared economy with utility tokens, where altcoins secure and reward useful work. However, the majority of the systems being developed, does not provide mechanisms to pair workers and clients, or rely on manual and insecure resolution. AStERISK bridges this gap allowing to perform sealed-bid auctions on blockchains, automatically determine the most optimal price for services, and assign clients to the most suitable workers. AStERISK allows workers to specify a minimal price for their work, and hide submitted b…
An Efficient and Multi-private Key Secure Aggregation for Federated Learning
Southwest Jiaotong University（西南交通大学）；Canadian Institute of Cybersecurity（加拿大网络安全研究所）；University of New Brunswick（新不伦瑞克大学）；DBAPPSecurity Ltd（杭州迪普科技股份有限公司）
National Tsing Hua University（国立清华大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Federated learning (FL) is a new paradigm that enables many clients to jointly train a machine learning (ML) model under the orchestration of a parameter server while keeping the local data not being exposed to any third party. However, the training of FL is an interactive process between local clients and the parameter server. Such process would cause privacy leakage since adversaries may retrieve sensitive information by analyzing the overheard messages. In this paper, we propose a new federated stochastic primal-dual algorithm with differential privacy (FedSPD-DP). Compared to the existing …
Delft University of Technology（代尔夫特理工大学）；University of Greenwich（格林威治大学）
In this work, we propose FLVoogd, an updated federated learning method in which servers and clients collaboratively eliminate Byzantine attacks while preserving privacy. In particular, servers use automatic Density-based Spatial Clustering of Applications with Noise (DBSCAN) combined with S2PC to cluster the benign majority without acquiring sensitive personal information. Meanwhile, clients build dual models and perform test-based distance controlling to adjust their local models toward the global one to achieve personalizing. Our framework is automatic and adaptive that servers/clients don't…
The application and analysis of the Cut-and-Choose technique in protocols secure against quantum adversaries is not a straightforward transposition of the classical case, among other reasons due to the difficulty to use rewinding in the quantum realm. We introduce a Quantum Computation Cut-and-Choose (QC-CC) technique which is a generalisation of the classical Cut-and-Choose in order to build quantum protocols secure against quantum covert adversaries. Such adversaries can deviate arbitrarily provided that their deviation is not detected. As an application of the QC-CC we give a protocol for s…
Significant amounts of data are currently being stored and managed on third-party servers. It is impractical for many small scale enterprises to own their private datacenters, hence renting third-party servers is a viable solution for such businesses. But the increasing number of malicious attacks, both internal and external, as well as buggy software on third-party servers is causing clients to lose their trust in these external infrastructures. While small enterprises cannot avoid using external infrastructures, they need the right set of protocols to manage their data on untrusted infrastru…
Secure multi-party computation (MPC) is a general cryptographic technique that allows distrusting parties to compute a function of their individual inputs, while only revealing the output of the function. It has found applications in areas such as auctioning, email filtering, and secure teleconference. Given its importance, it is crucial that the protocols are specified and implemented correctly. In the programming language community it has become good practice to use computer proof assistants to verify correctness proofs. In the field of cryptography, EasyCrypt is the state of the art proof a…
The universal blind quantum computation protocol (UBQC) (Broadbent, Fitzsimons, Kashefi 2009) enables an almost classical client to delegate a quantum computation to an untrusted quantum server (in form of a garbled quantum computation) while the security for the client is unconditional. In this contribution we explore the possibility of extending the verifiable UBQC (Fitzsimons, Kashefi 2012), to achieve further functionalities as was done for classical garbled computation. First, exploring the asymmetric nature of UBQC (client preparing only single qubits, while the server runs the entire qu…
With the emergence of cloud computing services, computationally weak devices (Clients) can delegate expensive tasks to more powerful entities (Servers). This raises the question of verifying a result at a lower cost than that of recomputing it. This verification can be private, between the Client and the Server, or public, when the result can be verified by any third party. We here present protocols for the verification of matrix-vector multiplications, that are secure against malicious Servers. The obtained algorithms are essentially optimal in the amortized model: the overhead for the Server…
ScionFL: Efficient and Robust Secure Quantized Aggregation
VMware Research Group（威睿研究集团）；Technical University of Darmstadt（达姆施塔特工业大学）；Aptos Labs（Aptos实验室）；Bar-Ilan University（巴伊兰大学）；Technology Innovation Institute（技术创新研究院）；DFINITY Foundation（DFINITY基金会）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）
