Privacy has become a serious concern for modern Information Societies. The sensitive nature of much of the data that are daily exchanged or released to untrusted parties requires that responsible organizations undertake appropriate privacy protection measures. Nowadays, much of these data are texts (e.g., emails, messages posted in social media, healthcare outcomes, etc.) that, because of their unstructured and semantic nature, constitute a challenge for automatic data protection methods. In fact, textual documents are usually protected manually, in a process known as document redaction or sanitization. To do so, human experts identify sensitive terms (i.e., terms that may reveal identities and/or confidential information) and protect them accordingly (e.g., via removal or, preferably, generalization). To relieve experts from this burdensome task, in a previous work we introduced the theoretical basis of C-sanitization, an inherently semantic privacy model that provides the basis to the development of automatic document redaction/sanitization algorithms and offers clear and a priori privacy guarantees on data protection; even though its potential benefits C-sanitization still presents some limitations when applied to practice (mainly regarding flexibility, efficiency and accuracy). In this paper, we propose a new more flexible model, named (C, g(C))-sanitization, which enables an intuitive configuration of the trade-off between the desired level of protection (i.e., controlled information disclosure) and the preservation of the utility of the protected data (i.e., amount of semantics to be preserved). Moreover, we also present a set of technical solutions and algorithms that provide an efficient and scalable implementation of the model and improve its practical accuracy, as we also illustrate through empirical experiments.
Within the current context of Information Societies, large amounts of information are daily exchanged and/or released. The sensitive nature of much of this information causes a serious privacy threat when documents are uncontrollably made available to untrusted third parties. In such cases, appropriate data protection measures should be undertaken by the responsible organization, especially under the umbrella of current legislations on data privacy. To do so, human experts are usually requested to redact or sanitize document contents. To relieve this burdensome task, this paper presents a priv…
Investigative journalists collect large numbers of digital documents during their investigations. These documents can greatly benefit other journalists' work. However, many of these documents contain sensitive information. Hence, possessing such documents can endanger reporters, their stories, and their sources. Consequently, many documents are used only for single, local, investigations. We present DatashareNetwork, a decentralized and privacy-preserving search system that enables journalists worldwide to find documents via a dedicated network of peers. DatashareNetwork combines well-known an…
Differential privacy (DP) is a neat privacy definition that can co-exist with certain well-defined data uses in the context of interactive queries. However, DP is neither a silver bullet for all privacy problems nor a replacement for all previous privacy models. In fact, extreme care should be exercised when trying to extend its use beyond the setting it was designed for. This paper reviews the limitations of DP and its misuse for individual data collection, individual data release, and machine learning.
This document is a response to a report from the University of Melbourne on the privacy of the Opal dataset release. The Opal dataset was released by Data61 (CSIRO) in conjunction with the Transport for New South Wales (TfNSW). The data consists of two separate weeks of "tap-on/tap-off" data of individuals who used any of the four different modes of public transport from TfNSW: buses, light rail, train and ferries. These taps are recorded through the smart ticketing system, known as Opal, available in the state of New South Wales, Australia.
Spontaneous reporting systems (SRS) have been developed to collect adverse event records that contain personal demographics and sensitive information like drug indications and adverse reactions. The release of SRS data may disclose the privacy of the data provider. Unlike other microdata, very few anonymyization methods have been proposed to protect individual privacy while publishing SRS data. MS(k, θ*)-bounding is the first privacy model for SRS data that considers multiple individual records, mutli-valued sensitive attributes, and rare events. PPMS(k, θ*)-bounding then is proposed for solvi…
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 …
Is External Database Protection Static in Retrieval-Augmented Generation? Rethinking Privacy Preservation under Dynamic Queries
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机学院）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To alleviate privacy-related risks, several privacy preservation techniques have been proposed. Differential privacy is one of these techniques which provides strong privacy guarantees. However, the proposed techniques presume that event data …
Dynamic searchable symmetric encryption (DSSE) is a useful cryptographic tool in encrypted cloud storage. However, it has been reported that DSSE usually suffers from file-injection attacks and content leak of deleted documents. To mitigate these attacks, forward privacy and backward privacy have been proposed. Nevertheless, the existing forward/backward-private DSSE schemes can only support single keyword queries. To address this problem, in this paper, we propose two DSSE schemes supporting range queries. One is forward-private and supports a large number of documents. The other can achieve …
The recent SARS-CoV-2 pandemic gave rise to management approaches using mobile apps for contact tracing. The corresponding apps track individuals and their interactions, to facilitate alerting users of potential infections well before they become infectious themselves. Naive implementation obviously jeopardizes the privacy of health conditions, location, activities, and social interaction of its users. A number of protocol designs for colocation tracking have already been developed, most of which claim to function in a privacy preserving manner. However, despite claims such as "GDPR compliance…
Mobile healthcare system integrating wearable sensing and wireless communication technologies continuously monitors the users' health status. However, the mHealth system raises a severe privacy concern as the data it collects are private information, such as heart rate and blood pressure. In this paper, we propose an efficient and privacy-preserving mHealth data release approach for the statistic data with the objectives to preserve the unique patterns in the original data bins. The proposed approach adopts the bucket partition algorithm and the differential privacy algorithm for privacy prese…
The application of graph analytics to various domains has yielded tremendous societal and economical benefits in recent years. However, the increasingly widespread adoption of graph analytics comes with a commensurate increase in the need to protect private information in graph data, especially in light of the many privacy breaches in real-world graph data that was supposed to preserve sensitive information. This paper provides a comprehensive survey of private graph data release algorithms that seek to achieve the fine balance between privacy and utility, with a specific focus on provably pri…
We address the problem of how to "obfuscate" texts by removing stylistic clues which can identify authorship, whilst preserving (as much as possible) the content of the text. In this paper we combine ideas from "generalised differential privacy" and machine learning techniques for text processing to model privacy for text documents. We define a privacy mechanism that operates at the level of text documents represented as "bags-of-words" - these representations are typical in machine learning and contain sufficient information to carry out many kinds of classification tasks including topic iden…
The problem of obfuscating the authorship of a text document has received little attention in the literature to date. Current approaches are ad-hoc and rely on assumptions about an adversary's auxiliary knowledge which makes it difficult to reason about the privacy properties of these methods. Differential privacy is a well-known and robust privacy approach, but its reliance on the notion of adjacency between datasets has prevented its application to text document privacy. However, generalised differential privacy permits the application of differential privacy to arbitrary datasets endowed wi…
Carnegie Mellon University（卡内基梅隆大学）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；INESC-ID（葡萄牙系统与计算机工程研究所）
State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties. In this paper we propose a privacy-preserving approach to multi-document summarization. Our approach enables other parties to obtain summaries without learning anything else about the original documents' content. We use a hashing scheme known as Secure Binary Embeddings to convert documents representation containing key phrases and bag-of-words into bit strings, allowing the computation of approximate distances, in…
Time series data have numerous applications in big data analytics. However, they often cause privacy issues when collected from individuals. To address this problem, most existing works perturb the values in the time series while retaining their temporal order, which may lead to significant distortion of the values. Recently, we propose TLDP model that perturbs temporal perturbation to ensure privacy guarantee while retaining original values. It has shown great promise to achieve significantly higher utility than value perturbation mechanisms in many time series analysis. However, its practica…
Today, mobile data owners lack consent and control over the release and utilization of their location data. Third party applications continuously process and access location data without data owners granular control and without knowledge of how location data is being used. The proliferation of IoT devices will lead to larger scale abuses of trust. In this paper we present the first design and implementation of a privacy module built into the GPSD daemon. The GPSD daemon is a low-level GPS interface that runs on GPS enabled devices. The integration of the privacy module ensures that data owners…
There is an increasing demand to make data "open" to third parties, as data sharing has great benefits in data-driven decision making. However, with a wide variety of sensitive data collected, protecting privacy of individuals, communities and organizations, is an essential factor in making data "open". The approaches currently adopted by industry in releasing private data are often ad hoc and prone to a number of attacks, including re-identification attacks, as they do not provide adequate privacy guarantees. While differential privacy has attracted significant interest from academia and indu…
Several companies (e.g., Meta, Google) have initiated "data-for-good" projects where aggregate location data are first sanitized and released publicly, which is useful to many applications in transportation, public health (e.g., COVID-19 spread) and urban planning. Differential privacy (DP) is the protection model of choice to ensure the privacy of the individuals who generated the raw location data. However, current solutions fail to preserve data utility when each individual contributes multiple location reports (i.e., under user-level privacy). To offset this limitation, public releases by …
Being able to release and exploit open data gathered in information systems is crucial for researchers, enterprises and the overall society. Yet, these data must be anonymized before release to protect the privacy of the subjects to whom the records relate. Differential privacy is a privacy model for anonymization that offers more robust privacy guarantees than previous models, such as $k$-anonymity and its extensions. However, it is often disregarded that the utility of differentially private outputs is quite limited, either because of the amount of noise that needs to be added to obtain them…
The problem of the release of anonymized microdata is an important topic in the fields of statistical disclosure control (SDC) and privacy preserving data publishing (PPDP), and yet it remains sufficiently unsolved. In these research fields, k-anonymity has been widely studied as an anonymity notion for mainly deterministic anonymization algorithms, and some probabilistic relaxations have been developed. However, they are not sufficient due to their limitations, i.e., being weaker than the original k-anonymity or requiring strong parametric assumptions. First we propose Pk-anonymity, a new pro…
We propose a practical methodology to protect a user's private data, when he wishes to publicly release data that is correlated with his private data, in the hope of getting some utility. Our approach relies on a general statistical inference framework that captures the privacy threat under inference attacks, given utility constraints. Under this framework, data is distorted before it is released, according to a privacy-preserving probabilistic mapping. This mapping is obtained by solving a convex optimization problem, which minimizes information leakage under a distortion constraint. We addre…
Motivated by understanding the dynamics of sensitive social networks over time, we consider the problem of continual release of statistics in a network that arrives online, while preserving privacy of its participants. For our privacy notion, we use differential privacy -- the gold standard in privacy for statistical data analysis. The main challenge in this problem is maintaining a good privacy-utility tradeoff; naive solutions that compose across time, as well as solutions suited to tabular data either lead to poor utility or do not directly apply. In this work, we show that if there is a pu…
Process performance indicators (PPIs) are metrics to quantify the degree with which organizational goals defined based on business processes are fulfilled. They exploit the event logs recorded by information systems during the execution of business processes, thereby providing a basis for process monitoring and subsequent optimization. However, PPIs are often evaluated on processes that involve individuals, which implies an inevitable risk of privacy intrusion. In this paper, we address the demand for privacy protection in the computation of PPIs. We first present a framework that enforces con…
A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data. To overcome this challenge, we utilize the Diaconis-Freedman-Meckes (DFM) effect, which states that most projections of high-dimensional data are nearly Gaussian. Hence, we propose the RON-Gauss model that leverages the novel combination of dimensionality reduction via random orthonormal (RON) projection and the Gaussian generative model for synthesizing differentially-private data. We analyze how RON-Gauss benefits from the DFM effect, and present multiple …
In this document, we define the NADA security architecture based on refined use case scenarios, a derived high level model and security analysis. For the architecure design and verification we are applying the well known STRIDE model.
Kyoto University（京都大学）；National Institute of Information and Communications Technology（国立信息与通信技术研究所）
With the development of smart devices, such as the Amazon Echo and Apple's HomePod, speech data have become a new dimension of big data. However, privacy and security concerns may hinder the collection and sharing of real-world speech data, which contain the speaker's identifiable information, i.e., voiceprint, which is considered a type of biometric identifier. Current studies on voiceprint privacy protection do not provide either a meaningful privacy-utility trade-off or a formal and rigorous definition of privacy. In this study, we design a novel and rigorous privacy metric for voiceprint p…
Distributed data sharing in dynamic networks is ubiquitous. It raises the concern that the private information of dynamic networks could be leaked when data receivers are malicious or communication channels are insecure. In this paper, we propose to intentionally perturb the inputs and outputs of a linear dynamic system to protect the privacy of target initial states and inputs from released outputs. We formulate the problem of perturbation design as an optimization problem which minimizes the cost caused by the added perturbations while maintaining system controllability and ensuring the priv…
We introduce the problem of releasing sensitive data under differential privacy when the privacy level is subject to change over time. Existing work assumes that privacy level is determined by the system designer as a fixed value before sensitive data is released. For certain applications, however, users may wish to relax the privacy level for subsequent releases of the same data after either a re-evaluation of the privacy concerns or the need for better accuracy. Specifically, given a database containing sensitive data, we assume that a response $y_1$ that preserves $ε_{1}$-differential priva…
University of Helsinki（赫尔辛基大学）；Computer Vision Center, Universitat Autònoma de Barcelona（巴塞罗那自治大学计算机视觉中心）；CISPA Helmholtz Center for Information Security（信息安全赫尔姆霍茨中心）；INRIA（法国国家信息与自动化技术研究所）；Yooz；Carnegie Mellon University（卡内基梅隆大学）；NTT（日本NTT公司）；Institute of Science Tokyo（东京科学研究所）；Department of Computer Science（计算机科学系）；Mphasis AI & Applied Tech Lab at Ashoka, Ashoka University（阿什oka大学人工智能与应用技术实验室）；Koita Centre for Digital Health - Ashoka (KCDH-A)（阿什oka数字健康中心（KCDH-A））；Trivedi School of Biosciences, Ashoka University（阿什oka大学Trivedi生物科学学院）
Organizations often collect private data and release aggregate statistics for the public's benefit. If no steps toward preserving privacy are taken, adversaries may use released statistics to deduce unauthorized information about the individuals described in the private dataset. Differentially private algorithms address this challenge by slightly perturbing underlying statistics with noise, thereby mathematically limiting the amount of information that may be deduced from each data release. Properly calibrating these algorithms -- and in turn the disclosure risk for people described in the dat…
