With the rapid development of GPS enabled devices (smartphones) and location-based applications, location privacy is increasingly concerned. Intuitively, it is widely believed that location privacy can be preserved by publishing aggregated mobility data, such as the number of users in an area at some time. However, a recent attack shows that these aggregated mobility data can be exploited to recover individual trajectories. In this paper, we first propose two differentially private basic schemes for aggregated mobility data publication, namely direct perturbation and threshold perturbation, which preserve location privacy of users and especially resist the trajectory recovery attack. Then, we explore the moving characteristics of mobile users, and design an improved scheme named static hybrid perturbation by combining the two basic schemes according to the moving characteristics. Since static hybrid perturbation works only for static data, which are entirely available before publishing, we further adapt the static hybrid perturbation by combining it with linear regression, and yield another improved scheme named dynamic hybrid perturbation. The dynamic hybrid perturbation works also for dynamic data, which are generated on the fly during publication. Privacy analysis shows that the proposed schemes achieve differential privacy. Extensive experiments on both simulated and real datasets demonstrate that all proposed schemes resist the trajectory recovery attack well, and the improved schemes significantly outperform the basic schemes.
Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive attributes, can result in privacy leakage. To enable data analysis, a strong notion of privacy is required to avoid risking unintended privacy violations. Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Desp…
A large amount of high-dimensional and heterogeneous data appear in practical applications, which are often published to third parties for data analysis, recommendations, targeted advertising, and reliable predictions. However, publishing these data may disclose personal sensitive information, resulting in an increasing concern on privacy violations. Privacy-preserving data publishing has received considerable attention in recent years. Unfortunately, the differentially private publication of high dimensional data remains a challenging problem. In this paper, we propose a differentially privat…
Location entropy (LE) is a popular metric for measuring the popularity of various locations (e.g., points-of-interest). Unlike other metrics computed from only the number of (unique) visits to a location, namely frequency, LE also captures the diversity of the users' visits, and is thus more accurate than other metrics. Current solutions for computing LE require full access to the past visits of users to locations, which poses privacy threats. This paper discusses, for the first time, the problem of perturbing location entropy for a set of locations according to differential privacy. The probl…
Inria（法国国家信息与自动化研究所）；École Polytechnique (IPP)（巴黎综合理工学院（巴黎理工学院））；Femto-ST Institute（弗朗什-孔泰电子、力学、热学与光学科学技术研究所）；Univ. Bourg. Franche-Comté（勃艮第-弗朗什-孔泰大学）；UBFC（勃艮第-弗朗什-孔泰大学联盟）；CNRS（法国国家科学研究中心）；Orange Applications For Business（Orange商业应用公司）；Orange Labs.（奥兰治实验室）；Antonine University Hadat-Baabda（哈达特-巴卜达安东尼大学）；National University of Singapore（新加坡国立大学）
This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered \textit{gradient perturbation}, which uses the differentially private stochastic gradient descent algorithm to guarantee the privacy of each time series sample in the learning stage. On the other hand, we considered \textit{input perturbation}, which add…
Conventional private data publication mechanisms aim to retain as much data utility as possible while ensuring sufficient privacy protection on sensitive data. Such data publication schemes implicitly assume that all data analysts and users have the same data access privilege levels. However, it is not applicable for the scenario that data users often have different levels of access to the same data, or different requirements of data utility. The multi-level privacy requirements for different authorization levels pose new challenges for private data publication. Traditional PPDP mechanisms onl…
This paper presents a new privacy-preserving smart metering system. Our scheme is private under the differential privacy model and therefore provides strong and provable guarantees. With our scheme, an (electricity) supplier can periodically collect data from smart meters and derive aggregated statistics while learning only limited information about the activities of individual households. For example, a supplier cannot tell from a user's trace when he watched TV or turned on heating. Our scheme is simple, efficient and practical. Processing cost is very limited: smart meters only have to add …
In the area of industrial process mining, privacy-preserving event data publication is becoming increasingly relevant. Consequently, the trade-off between high data utility and quantifiable privacy poses new challenges. State-of-the-art research mainly focuses on differentially private trace variant construction based on prefix expansion methods. However, these algorithms face several practical limitations such as high computational complexity, introducing fake variants, removing frequent variants, and a bounded variant length. In this paper, we introduce a new approach for direct differential…
Tencent Medical AI Lab（腾讯医疗人工智能实验室）；Georgia State University（佐治亚州立大学）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；State University of New York at Buffalo（纽约州立大学布法罗分校）；Alibaba Group（阿里巴巴集团）
Nowadays, crowd sensing becomes increasingly more popular due to the ubiquitous usage of mobile devices. However, the quality of such human-generated sensory data varies significantly among different users. To better utilize sensory data, the problem of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has emerged as a hot topic. Although the existing truth discovery approaches can provide reliable aggregated results, they fail to protect the private information of individual users. Moreover, crowd sensing syst…
This paper proposes a framework to investigate the value of sharing privacy-protected smart meter data between domestic consumers and load serving entities. The framework consists of a discounted differential privacy model to ensure individuals cannot be identified from aggregated data, a ANN-based short-term load forecasting to quantify the impact of data availability and privacy protection on the forecasting error and an optimal procurement problem in day-ahead and balancing markets to assess the market value of the privacy-utility trade-off. The framework demonstrates that when the load pro…
Age Aware Scheduling for Differentially-Private Federated Learning
Institute of Communications Engineering, National Yang Ming Chiao Tung University（国立阳明交通大学通信工程研究所）；Simula UiB（西挪威应用科技大学西蒙研究所）；Department of Communication Engineering, National Taipei University（国立台北大学通信工程学系）
Local differential privacy (LDP) can provide each user with strong privacy guarantees under untrusted data curators while ensuring accurate statistics derived from privatized data. Due to its powerfulness, LDP has been widely adopted to protect privacy in various tasks (e.g., heavy hitters discovery, probability estimation) and systems (e.g., Google Chrome, Apple iOS). Although $ε$-LDP has been proposed for many years, the more general notion of $(ε, δ)$-LDP has only been studied in very few papers, which mainly consider mean estimation for numeric data. Besides, prior solutions achieve $(ε, δ…
In social networks, different users may have different privacy preferences and there are many users with public identities. Most work on differentially private social network data publication neglects this fact. We aim to release the number of public users that a private user connects to within n hops, called n-range Connection fingerprints(CFPs), under user-level personalized privacy preferences. We proposed two schemes, Distance-based exponential budget absorption (DEBA) and Distance-based uniformly budget absorption using Ladder function (DUBA-LF), for privacy-preserving publication of the …
University of Connecticut（康涅狄格大学）；CWI Amsterdam（阿姆斯特丹数学与计算机科学研究中心）；IBM Research（IBM研究院）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）
The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication. Most newly launched smartphones and IoT devices are equipped with GPUs or sufficient computing hardware to run powerful AI models. However, in case of the original synchronous federated learning, client devices suffer waiting times and regular communication between clients and server is required. This implies more sensitivity to local model training times and irregular or missed updates, hence, less or limited scalability to large numbers of clients and convergenc…
Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy. It requires workers to arrive at a particular location for task fulfillment. Effective protection of location privacy is essential for workers' enthusiasm and valid task assignment. However, existing SC models with differential privacy usually perturb real-time location data for both partition and data publication. Such a way may produce large perturbations to counting queries that affect assignment success rate and allocation accuracy. This paper proposes a framewor…
Idiap Research Institute（伊迪亚普研究所）；EPFL（洛桑联邦理工学院）；The Alan Turing Institute（艾伦·图灵研究所）；Inria（法国国家信息与自动化研究所）
In this paper, we study the problem of learning Graph Neural Networks (GNNs) with Differential Privacy (DP). We propose a novel differentially private GNN based on Aggregation Perturbation (GAP), which adds stochastic noise to the GNN's aggregation function to statistically obfuscate the presence of a single edge (edge-level privacy) or a single node and all its adjacent edges (node-level privacy). Tailored to the specifics of private learning, GAP's new architecture is composed of three separate modules: (i) the encoder module, where we learn private node embeddings without relying on the edg…
DP-FedSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix
Department of Mechanical Engineering, Indian Institute of Technology Delhi（印度理工学院德里机械工程系）；SQC & OR Unit, Indian Statistical Institute Kolkata（印度统计研究院科钦SQC与OR单位）；Department of Artificial Intelligence, Indian Institute of Technology Hyderabad（印度理工学院海得拉巴人工智能系）；Operations Management & Quantitative Techniques Area, Indian Institute of Management, Indore（印度管理学院印地尔运营管理和定量技术领域）
DP-ES: Differentially Private Evolution Strategies for Prompt Optimization
National University of Defense Technology（国防科技大学）；CRRC Zhuzhou Electric Locomotive Research Institute Co., Ltd.（中车株洲电力机车研究所有限公司）；China Academy of Railway Sciences（中国铁道科学研究院）
Google Research（谷歌研究院）；Tel Aviv University（特拉维夫大学）；Stanford University（斯坦福大学）；Ben-Gurion University（本-古里安大学）
Common datasets have the form of elements with keys (e.g., transactions and products) and the goal is to perform analytics on the aggregated form of key and frequency pairs. A weighted sample of keys by (a function of) frequency is a highly versatile summary that provides a sparse set of representative keys and supports approximate evaluations of query statistics. We propose private weighted sampling (PWS): A method that ensures element-level differential privacy while retaining, to the extent possible, the utility of a respective non-private weighted sample. PWS maximizes the reporting probab…
Criteo AI Lab（Criteo AI实验室）；Meta Platforms, Inc.（Meta平台公司）；Criteo AI Lab and Hui Yang Affiliation: Meta Platforms, Inc.（Criteo AI实验室）
Designing data sharing mechanisms providing performance and strong privacy guarantees is a hot topic for the Online Advertising industry. Namely, a prominent proposal discussed under the Improving Web Advertising Business Group at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21, a premier workshop on Advertising Science with data provided by advertising company Criteo. In this paper, we describe the challenge tasks, th…
Data Poisoning Attacks to Locally Differentially Private Range Query Protocols
National Yang Ming Chiao Tung University（国立阳明交通大学）；RIKEN AIP（理化学研究所人工智能项目）；ISM and AIST（信息科学研究机构与产业技术综合研究所）；Institute of Science Tokyo（东京科学大学）；Meiji University（明治大学）
Highly accurate profiles of consumers daily energy usage are reported to power grid via smart meters which enables smart grid to effectively regulate power demand and supply. However, consumers energy consumption pattern can reveal personal and sensitive information regarding their lifestyle. Therefore, to ensure users privacy, differentially distributed noise is added to the original data. This technique comes with a trade off between privacy of the consumer versus utility of the data in terms of providing services like billing, Demand Response schemes, and Load Monitoring. In this paper, we …
In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, w…
Technical University of Munich（慕尼黑工业大学）；Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Imperial College London（伦敦帝国理工学院）
In this work, we study the applications of differential privacy (DP) in the context of graph-structured data. We discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). The formulation of DP in the context of graph-structured data is difficult, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by a…
SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
University of Texas at Dallas Richardson, TX 75082（德克萨斯大学达拉斯分校）；Vanderbilt University Medical Center Nashville, TN 37232（范德比尔特大学医学中心）；Virginia Tech Blacksburg, VA 24061（弗吉尼亚理工大学）
The era of Big Data has brought with it a richer understanding of user behavior through massive data sets, which can help organizations optimize the quality of their services. In the context of transportation research, mobility data can provide Municipal Authorities (MA) with insights on how to operate, regulate, or improve the transportation network. Mobility data, however, may contain sensitive information about end users and trade secrets of Mobility Providers (MP). Due to this data privacy concern, MPs may be reluctant to contribute their datasets to MA. Using ideas from cryptography, we p…
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；The Chinese University of Hong Kong（香港中文大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Northwestern University（西北大学）
Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other's data but are prevented from doing so due to privacy regulations. Some regulations prevent explicit sharing of data between parties by joining datasets in a central location (confidentiality). Others also limit implicit sharing of data, e.g., through model predictions (privacy). There is currently no method that …
