SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication

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. Despite this trade-off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade-off and how to address it appropriately. Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we identify trends and connections in the contributions to the field of differential privacy for histograms and synthetic data and 2) we provide an understanding of the privacy/accuracy trade-off challenge by crystallizing different dimensions to accuracy improvement. Accordingly, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy

Privacy, Space and Time: a Survey on Privacy-Preserving Continuous Data Publishing

Sensors, portable devices, and location-based services, generate massive amounts of geo-tagged, and/or location- and user-related data on a daily basis. The manipulation of such data is useful in numerous application domains, e.g., healthcare, intelligent buildings, and traffic monitoring, to name a few. A high percentage of these data carry information of users\u27 activities and other personal details, and thus their manipulation and sharing arise concerns about the privacy of the individuals involved. To enable the secure—from the users\u27 privacy perspective—data sharing, researchers have already proposed various seminal techniques for the protection of users\u27 privacy. However, the continuous fashion in which data are generated nowadays, and the high availability of external sources of information, pose more threats and add extra challenges to the problem. In this survey, we visit the works done on data privacy for continuous data publishing, and report on the proposed solutions, with a special focus on solutions concerning location or geo-referenced data

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure

Trading Indistinguishability-based Privacy and Utility of Complex Data

The collection and processing of complex data, like structured data or infinite streams, facilitates novel applications. At the same time, it raises privacy requirements by the data owners. Consequently, data administrators use privacy-enhancing technologies (PETs) to sanitize the data, that are frequently based on indistinguishability-based privacy definitions. Upon engineering PETs, a well-known challenge is the privacy-utility trade-off. Although literature is aware of a couple of trade-offs, there are still combinations of involved entities, privacy definition, type of data and application, in which we miss valuable trade-offs. In this thesis, for two important groups of applications processing complex data, we study (a) which indistinguishability-based privacy and utility requirements are relevant, (b) whether existing PETs solve the trade-off sufficiently, and (c) propose novel PETs extending the state-of-the-art substantially in terms of methodology, as well as achieved privacy or utility. Overall, we provide four contributions divided into two parts. In the first part, we study applications that analyze structured data with distance-based mining algorithms. We reveal that an essential utility requirement is the preservation of the pair-wise distances of the data items. Consequently, we propose distance-preserving encryption (DPE), together with a general procedure to engineer respective PETs by leveraging existing encryption schemes. As proof of concept, we apply it to SQL log mining, useful for database performance tuning. In the second part, we study applications that monitor query results over infinite streams. To this end, -event differential privacy is state-of-the-art. Here, PETs use mechanisms that typically add noise to query results. First, we study state-of-the-art mechanisms with respect to the utility they provide. Conducting the so far largest benchmark that fulfills requirements derived from limitations of prior experimental studies, we contribute new insights into the strengths and weaknesses of existing mechanisms. One of the most unexpected, yet explainable result, is a baseline supremacy. It states that one of the two baseline mechanisms delivers high or even the best utility. A natural follow-up question is whether baseline mechanisms already provide reasonable utility. So, second, we perform a case study from the area of electricity grid monitoring revealing two results. First, achieving reasonable utility is only possible under weak privacy requirements. Second, the utility measured with application-specific utility metrics decreases faster than the sanitization error, that is used as utility metric in most studies, suggests. As a third contribution, we propose a novel differential privacy-based privacy definition called Swellfish privacy. It allows tuning utility beyond incremental -event mechanism design by supporting time-dependent privacy requirements. Formally, as well as by experiments, we prove that it increases utility significantly. In total, our thesis contributes substantially to the research field, and reveals directions for future research

Towards Mobility Data Science (Vision Paper)

Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS-equipped mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and health sciences. In this paper, we present the emerging domain of mobility data science. Towards a unified approach to mobility data science, we envision a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. For each of these components, we explain how mobility data science differs from general data science, we survey the current state of the art and describe open challenges for the research community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from the metadata. PDF has not been change