Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially because LBS providers can be honest-but-curious, collecting queries and tracking users' whereabouts and infer sensitive user data. This motivated both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome disadvantages of centralized schemes, eliminating anonymizers, and enhancing users' control over sensitive information. However, an insecure decentralized system could create serious risks beyond private information leakage. More so, attacking an improperly designed decentralized LBS privacy protection scheme could be an effective and low-cost step to breach user privacy. We address exactly this problem, by proposing security enhancements for mobile data sharing systems. We protect user privacy while preserving accountability of user activities, leveraging pseudonymous authentication with mainstream cryptography. We show our scheme can be deployed with off-the-shelf devices based on an experimental evaluation of an implementation in a static automotive testbed.
Debuggers are a popular reverse engineering and tampering tool. Self-debugging is an effective technique for applications to defend themselves against hostile debuggers. In penetration tests on state-of-the-art self-debugging, we observed several opportunities through which it could be attacked, however. We therefore improved upon the existing technique, making it more resilient by introducing reciprocal debugging and making the transfers of control between protected application and self-debugger more stealthy. This paper presents the improved self-debugging design, and details our research ef…
We introduce a protection-based IP security scheme to protect soft and firm IP cores which are used on FPGA devices. The scheme is based on Finite State Machin (FSM) obfuscation and exploits Physical Unclonable Function (PUF) for FPGA unique identification (ID) generation which help pay-per-device licensing. We introduce a communication protocol to protect the rights of parties in this market. On standard benchmark circuits, the experimental results show that our scheme is secure, attack-resilient and can be implemented with low area, power and delay overheads.
An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids
Department of Electrical and Computer Engineering, University of Toronto（多伦多大学电气与计算机工程系）；Electric Power Engineering Department, Cairo University（开罗大学电力工程系）；Department of Electrical Engineering, Khalifa University（哈利法大学电气工程系）；Concordia Institute for Information Systems Engineering (CIISE), Concordia University（康卡迪亚大学信息系统工程研究所）
Logic locking has emerged as a prominent key-driven technique to protect the integrity of integrated circuits. However, novel machine-learning-based attacks have recently been introduced to challenge the security foundations of locking schemes. These attacks are able to recover a significant percentage of the key without having access to an activated circuit. This paper address this issue through two focal points. First, we present a theoretical model to test locking schemes for key-related structural leakage that can be exploited by machine learning. Second, based on the theoretical model, we…
Location-based Services (LBSs) provide valuable services, with convenient features for users. However, the information disclosed through each request harms user privacy. This is a concern particularly with honest-but-curious LBS servers, which could, by collecting requests, track users and infer additional sensitive user data. This is the motivation of both centralized and decentralized location privacy protection schemes for LBSs: anonymizing and obfuscating LBS queries to not disclose exact information, while still getting useful responses. Decentralized schemes overcome the disadvantages of…
CRSF: Collusion-Resilient Privacy-Preserving Sensor Fusion with Byzantine-Robust Participation
Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Centrum Wiskunde & Informatica（荷兰数学与计算机科学研究学会）；Southwest University（西南大学）；Yangtze Delta Region Institute of Tsinghua University（清华大学长三角研究院）；University of Trento（特伦托大学）
Security architectures providing Trusted Execution Environments (TEEs) have been an appealing research subject for a wide range of computer systems, from low-end embedded devices to powerful cloud servers. The goal of these architectures is to protect sensitive services in isolated execution contexts, called enclaves. Unfortunately, existing TEE solutions suffer from significant design shortcomings. First, they follow a one-size-fits-all approach offering only a single enclave type, however, different services need flexible enclaves that can adjust to their demands. Second, they cannot efficie…
University of Southern California（南加州大学）；University of California, Riverside（加州大学河滨分校）
Secure federated learning is a privacy-preserving framework to improve machine learning models by training over large volumes of data collected by mobile users. This is achieved through an iterative process where, at each iteration, users update a global model using their local datasets. Each user then masks its local model via random keys, and the masked models are aggregated at a central server to compute the global model for the next iteration. As the local models are protected by random masks, the server cannot observe their true values. This presents a major challenge for the resilience o…
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Monash University（莫纳什大学）；City University of Hong Kong（香港城市大学）；RMIT University（皇家墨尔本理工大学）
Federated learning has recently emerged as a paradigm promising the benefits of harnessing rich data from diverse sources to train high quality models, with the salient features that training datasets never leave local devices. Only model updates are locally computed and shared for aggregation to produce a global model. While federated learning greatly alleviates the privacy concerns as opposed to learning with centralized data, sharing model updates still poses privacy risks. In this paper, we present a system design which offers efficient protection of individual model updates throughout the…
Recently, a significant amount of interest has been developed in motivating physicians to use e-health technology (especially Electronic Medical Records (EMR) systems). An important utility of such EMR systems is: a next generation of Clinical Decision Support Systems (CDSS) will extract knowledge from these electronic medical records to enable physicians to do accurate and effective diagnosis. It is anticipated that in future such medical records will be shared through cloud among different physicians to improve the quality of health care. Therefore, right protection of medical records is imp…
University of California, Los Angeles（加州大学洛杉矶分校）；City University of Hong Kong（香港城市大学）
We study distributed optimization in the presence of Byzantine adversaries, where both data and computation are distributed among $m$ worker machines, $t$ of which may be corrupt. The compromised nodes may collaboratively and arbitrarily deviate from their pre-specified programs, and a designated (master) node iteratively computes the model/parameter vector for generalized linear models. In this work, we primarily focus on two iterative algorithms: Proximal Gradient Descent (PGD) and Coordinate Descent (CD). Gradient descent (GD) is a special case of these algorithms. PGD is typically used in …
Privacy-aware Berrut Approximated Coded Computing applied to general distributed learning
Galician Research and Development Center in Advanced Telecommunications (GRADIANT)（加利西亚先进电信研究与发展中心（GRADIANT））；atlanTTic, Information & Computing Lab, Telecommunication Engineering School, Universidade de Vigo（atlanTTic信息与计算实验室，电信工程学院，维戈大学）
Billions of devices in the Internet of Things (IoT) collect sensitive data about people, creating data privacy risks and breach vulnerabilities. Accordingly, data privacy preservation is vital for sustaining the proliferation of IoT services. In particular, privacy-preserving IoT connects devices embedded with sensors and maintains the data privacy of people. However, common misconceptions exist among IoT researchers, service providers, and users about privacy-preserving IoT. This article refutes five common misconceptions about privacy-preserving IoT concerning data sensing and innovation, re…
A movement for a more transparent and decentralized Internet is globally attracting more attention. People are becoming more privacy-aware of their online identities and data. The Internet is constantly evolving. Web2 focused on companies that provide services in exchange for personal user data. Web3 commits to user-centricity using decentralization and zero-server architectures. The current digital society demands a global change to empower citizens and take back control. Citizens are locked into big-tech for personal data storage and their for-profit digital identity. Protection of data has …
