Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. In this paper, we demonstrate practical verification of a cryptographic protocol client's messaging behavior as being consistent with the client program it is believed to be running. Moreover, we accomplish this without modifying the client in any way, and without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client's messages explores multiple candidate execution paths in the client concurrently, an innovation that we show is both specifically useful for cryptographic protocol clients and more generally useful for client applications of other types, as well. In addition, our toolchain includes a novel approach to symbolically executing the client software in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet, and that verification of legitimate clients can keep pace with, e.g., Gmail workloads.
In this article, we provide a summary of recent efforts towards achieving Internet geolocation securely, \ie without allowing the entity being geolocated to cheat about its own geographic location. Cheating motivations arise from many factors, including impersonation (in the case locations are used to reinforce authentication), and gaining location-dependent benefits. In particular, we provide a technical overview of Client Presence Verification (CPV) and Server Location Verification (SLV)---two recently proposed techniques designed to verify the geographic locations of clients and servers in …
In key agreement protocols, the user will send a request to the server and the server will respond to that message. After two-way authentication, a secure session key will be created between them. They use the session key to create a secure channel for communication. In 2021, Saleem et al. proposed a protocol for securing user and server communications, claiming that their proposed protocol meets a variety of security needs and is also resistant to known types of attacks. In this article, we will show that Saleem et al's scheme does not meet the security requirement of perfect forward secrecy.
This paper presents Poplar, a new system for solving the private heavy-hitters problem. In this problem, there are many clients and a small set of data-collection servers. Each client holds a private bitstring. The servers want to recover the set of all popular strings, without learning anything else about any client's string. A web-browser vendor, for instance, can use Poplar to figure out which homepages are popular, without learning any user's homepage. We also consider the simpler private subset-histogram problem, in which the servers want to count how many clients hold strings in a partic…
Cloud service providers offer various facilities to their clients. The clients with limited resources opt for some of these facilities. They can outsource their bulk data to the cloud server. The cloud server maintains these data in lieu of monetary benefits. However, a malicious cloud server might delete some of these data to save some space and offer this extra amount of storage to another client. Therefore, the client might not retrieve her file (or some portions of it) as often as needed. Proofs of retrievability (POR) provide an assurance to the client that the server is actually storing …
Users' devices, e.g., smartphones or laptops, are typically incapable of securely storing and processing cryptographic keys. We present Tandem, a novel set of protocols for securing cryptographic keys with support from a central server. Tandem uses one-time-use key-share tokens to preserve users' privacy with respect to a malicious central server. Additionally, Tandem enables users to block their keys if they lose their device, and it enables the server to limit how often an adversary can use an unblocked key. We prove Tandem's security and privacy properties, apply Tandem to attribute-based c…
Decision trees are a powerful prediction model with many applications in statistics, data mining, and machine learning. In some settings, the model and the data to be classified may contain sensitive information belonging to different parties. In this paper, we, therefore, address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the server's private model. The goal of the computation is to obtain the classification while preservi…
Generating secure random numbers is vital to the security and privacy infrastructures we rely on today. Having a computer system generate a secure random number is not a trivial problem due to the deterministic nature of computer systems. Servers commonly deal with this problem through hardware-based random number generators, which can come in the form of expansion cards, dongles, or integrated into the CPU itself. With the explosion of network- and internet-connected devices, however, the problem of cryptography is no longer a server-centric problem; even small devices need a reliable source …
Enforcing Cryptographic Distributed-VCS Access Control with No Trust on Servers
Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Beijing Zitiao Network Technology Co., Ltd.（北京字跳网络技术有限公司）；University of Science and Technology of China（中国科学技术大学）；University of Kansas（堪萨斯大学）；Michigan Technological University（密歇根理工大学）
Decision forests are classical models to efficiently make decision on complex inputs with multiple features. While the global structure of the trees or forests is public, sensitive information have to be protected during the evaluation of some client inputs with respect to some server model. Indeed, the comparison thresholds on the server side may have economical value while the client inputs might be critical personal data. In addition, soundness is also important for the receiver. In our case, we will consider the server to be interested in the outcome of the model evaluation so that the cli…
Outsourcing computation has gained significant popularity in recent years due to the prevalence of cloud computing. There are two main security concerns in outsourcing computation: how to guarantee the cloud server performs the computation correctly and how to keep the client's data secret. The {\em single-server verifiable computation} (SSVC) of Gennaro, Gentry and Parno (Crypto'10) enables a client to delegate the computation of a function $f$ on any input $x$ with both concerns highly relieved, but only results in {\em computationally secure} schemes that {\em lack practical efficiency}. Wh…
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…
EIFL: Efficiently Protecting Global Model Privacy and Integrity Against an Untrusted Server in Federated Learning
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；Department of Computer Science, Illinois Institute of Technology（伊利诺伊理工学院计算机科学系）
Performing computations while maintaining privacy is an important problem in todays distributed machine learning solutions. Consider the following two set ups between a client and a server, where in setup i) the client has a public data vector $\mathbf{x}$, the server has a large private database of data vectors $\mathcal{B}$ and the client wants to find the inner products $\langle \mathbf{x,y_k} \rangle, \forall \mathbf{y_k} \in \mathcal{B}$. The client does not want the server to learn $\mathbf{x}$ while the server does not want the client to learn the records in its database. This is in con…
Robust and Actively Secure Serverless Collaborative Learning
Northwestern University（西北大学）；CISPA（CISPA亥姆霍兹信息安全中心）；Google（谷歌）；University of Toronto and Vector Institute（多伦多大学与向量研究所）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
University of Florida（佛罗里达大学）；Virginia Commonwealth University（弗吉尼亚联邦大学）
Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. …
Homomorphic secret sharing (HSS) allows multiple input clients to secret-share their data among multiple servers such that each server is able to locally compute a function on its shares to obtain a partial result and all partial results enable the reconstruction of the function's value on the outsourced data by an output client. The existing HSS schemes for {\em high-degree} polynomials either {\em require a large number of servers} or {\em lack verifiability}, which is essential for ensuring the correctness of the outsourced computations. In this paper, we propose a two-server verifiable HSS…
Server-Enforced Watermarking in U-Shaped Split Federated Learning
Software Engineering Institute, East China Normal University（东华大学软件工程学院）；College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；College of Control Science and Engineering, China University of Petroleum (East China)（中国石油大学（华东）控制科学与工程学院）
Duke University（杜克大学）；University of Science and Technology of China（中国科学技术大学）
Federated learning is vulnerable to poisoning attacks in which malicious clients poison the global model via sending malicious model updates to the server. Existing defenses focus on preventing a small number of malicious clients from poisoning the global model via robust federated learning methods and detecting malicious clients when there are a large number of them. However, it is still an open challenge how to recover the global model from poisoning attacks after the malicious clients are detected. A naive solution is to remove the detected malicious clients and train a new global model fro…
Federated Learning (FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by a central server. By assigning a weight to each client based on the proportion of data instances it possesses, the rate of convergence to an accurate joint model can be greatly accelerated. Some previous works studied FL in a Byzantine setting, in which a fraction of the clients may send arbitrary or even malicious information regarding their model. However, these works either ignore the issue of data unbalancedness…
Local Data Quantity-Aware Weighted Averaging for Federated Learning with Dishonest Clients
College of Information Science and Technology, Donghua University（东华大学信息科学与技术学院）；School of Engineering, Westlake University（西湖大学工程学院）；College of Computing and Data Science, Nanyang Technological University (NTU)（南洋理工大学计算与数据科学学院）
The Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove t…
Sharif University of Technology（谢里夫理工大学）；Nokia Bell Labs（诺基亚贝尔实验室）
The emerging applications of machine learning algorithms on mobile devices motivate us to offload the computation tasks of training a model or deploying a trained one to the cloud or at the edge of the network. One of the major challenges in this setup is to guarantee the privacy of the client data. Various methods have been proposed to protect privacy in the literature. Those include (i) adding noise to the client data, which reduces the accuracy of the result, (ii) using secure multiparty computation (MPC), which requires significant communication among the computing nodes or with the client…
