Machine learning and privacy preserving algorithms for spatial and temporal sensing

Sensing physical and social environments are ubiquitous in modern mobile phones, IoT devices, and infrastructure-based settings. Information engraved in such data, especially the time and location attributes have unprecedented potential to characterize individual and crowd behaviour, natural and technological processes. However, it is challenging to extract abstract knowledge from the data due to its massive size, sequential structure, asynchronous operation, noisy characteristics, privacy concerns, and real time analysis requirements. Therefore, the primary goal of this thesis is to propose theoretically grounded and practically useful algorithms to learn from location and time stamps in sensor data. The proposed methods are inspired by tools from geometry, topology, and statistics. They leverage structures in the temporal and spatial data by probabilistically modeling noise, exploring topological structures embedded, and utilizing statistical structure to protect personal information and simultaneously learn aggregate information. Proposed algorithms are geared towards streaming and distributed operation for efficiency. The usefulness of the methods is argued using mathematical analysis and empirical experiments on real and artificial datasets

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

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks

Privacy-Preserved Linkable Social-Physical Data Publication

In this dissertation, we investigate the privacy-preserved data publication problems towards pervasively existing linkable social-physical contents. On the one hand, data publication has been considered as a critical approach to facilitate numerous utilities for individuals, populations, platform owners, and all third-party service providers. On the other hand, the unprecedented adoption of mobile devices and the dramatic development of Internet-of-Thing (IoT) systems have pushed the collection of surrounding physical information among populations to a totally novel stage. The collected contents can provide a fine-grained access to both physical and social aspects of the crowds, which introduces a comprehensively linkable and potentially sensitive information domain. The linkage includes the related index like privacy, utility, and efficiency for sophisticated applications, the inherent correlations among multiple data sources or information dimensions, and the connections among individuals. As the linkage leads to various novel challenges for privacy preservation, there should be a body of novel mechanisms for linkable social-physical data publications. As a result, this dissertation proposes a series of mechanisms for privacy-preserved linkable social-physical data publication. Firstly, we study the publication of physical data where the co-existing useful social proles and the sensitive physical proles of the data should be carefully maintained. Secondly, we investigate the data publication problem jointly considering the privacy preservation, data utility, and resource efficiency for task completion in crowd-sensing systems. Thirdly, we investigate the publication of private contents used for the recommendation, where contents of a user contribute to the recommendation results for others. Fourthly, we study the publications of reviews in local business service systems, where users expect to conceal their frequently visited locations while cooperatively maintain the utility of the whole system. Fifthly, we study the acquisition of privacy-preserved knowledge on cyber-physical social networks, where third-party service providers can derive the community structure without accessing the sensitive social links. We also provide detailed analysis and discussion for proposed mechanisms, and extensively validate their performance via real-world datasets. Both results demonstrate that the proposed mechanisms can properly preserve the privacy while maintaining the data utility. At last, we also propose the future research topics to complete the whole dissertation. The first topic focuses on the privacy preservation towards correlations beneath multiple data sources. The second topic studies more privacy issues for the whole population during data publication, including both the novel threats for related communities, and the disclosure of trends within crowds

NASA Tech Briefs, November 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences

LIPIcs, Volume 261, ICALP 2023, Complete Volume

LIPIcs, Volume 261, ICALP 2023, Complete Volum

PRIVACY PRESERVING DATA MINING FOR NUMERICAL MATRICES, SOCIAL NETWORKS, AND BIG DATA

Motivated by increasing public awareness of possible abuse of confidential information, which is considered as a significant hindrance to the development of e-society, medical and financial markets, a privacy preserving data mining framework is presented so that data owners can carefully process data in order to preserve confidential information and guarantee information functionality within an acceptable boundary. First, among many privacy-preserving methodologies, as a group of popular techniques for achieving a balance between data utility and information privacy, a class of data perturbation methods add a noise signal, following a statistical distribution, to an original numerical matrix. With the help of analysis in eigenspace of perturbed data, the potential privacy vulnerability of a popular data perturbation is analyzed in the presence of very little information leakage in privacy-preserving databases. The vulnerability to very little data leakage is theoretically proved and experimentally illustrated. Second, in addition to numerical matrices, social networks have played a critical role in modern e-society. Security and privacy in social networks receive a lot of attention because of recent security scandals among some popular social network service providers. So, the need to protect confidential information from being disclosed motivates us to develop multiple privacy-preserving techniques for social networks. Affinities (or weights) attached to edges are private and can lead to personal security leakage. To protect privacy of social networks, several algorithms are proposed, including Gaussian perturbation, greedy algorithm, and probability random walking algorithm. They can quickly modify original data in a large-scale situation, to satisfy different privacy requirements. Third, the era of big data is approaching on the horizon in the industrial arena and academia, as the quantity of collected data is increasing in an exponential fashion. Three issues are studied in the age of big data with privacy preservation, obtaining a high confidence about accuracy of any specific differentially private queries, speedily and accurately updating a private summary of a binary stream with I/O-awareness, and launching a mutual private information retrieval for big data. All three issues are handled by two core backbones, differential privacy and the Chernoff Bound

Aerospace Medicine and Biology: A continuing bibliography (supplement 160)

This bibliography lists 166 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1976

Differentially Private Data Analytics

With the emergence of smart devices and data-driven applications, personal data are being dramatically generated, gathered and used by modern systems for data analytics in a wide range of customised service applications. Despite the advantages of data analytics, potential risks arise that allow adversaries to infer individuals’ private information by using some auxiliary information. Therefore, it is crucial to develop new methods and techniques for privacy-preserving data analytics that ensure acceptable trade-offs between privacy and utility. Over the last decade, differential privacy (DP) has been viewed as a promising notion of privacy because it mathematically bounds the trade-off between privacy and utility against adversaries’ strong inference attacks (n−1 out of n items in the input). By exploring the latest results of differentially private data analytics, this thesis concentrates on four sub-topics: differentially private data aggregation with security guarantees, privacy budget guarantees in distributed systems, differentially private single-path publishing and differentially private k-means clustering. For differentially private data aggregation with security guarantees, we propose a twolayer data aggregation approach against semi-honest but colluding data aggregators, where DP noise is randomly injected. We address the problems of the collusion of data curators and data aggregators. The key idea of our approach is injecting DP noise randomly to prevent privacy disclosure from collusion, while maintaining a high degree of utility and splitting and sharing data pieces to guarantee security. The experimental evaluations over synthetic datasets confirm our mathematical analysis results and show that our approach achieves enhanced aggregation utility. For privacy budget guarantees in distributed systems, we study the parallel composition of privacy budget in differentially private data aggregation scenarios. We propose a new lower bound of the parallel composition of privacy budgets. We address two problems of the state-of-the-art: poor utility when using global sensitivity for both data curators and data aggregators and unknown privacy guarantees with conditions on the local sensitivities between data curators and data aggregators. The key is taking a property of the summation function: the local sensitivity of summation for a dataset is equal to the maximum summation of any sub-dataset. The experimental results over a real-life dataset support our theoretical results of the proposed lower bound of the unconditional parallel composition of privacy budget. For differentially private single-path publishing, we propose a graph-based single-path publishing approach with DP guarantees. We address two problems in existing work: information loss regarding the exact path for genuine users and no privacy guarantees for edges when adversaries have information about all vertices, yet one edge is missing. The main idea is adding fake vertices and edges into the real path by DP, and then hiding connections in the perturbed path in the topology of the published graph so that only the trusted path users with full knowledge about the map can recover the real path. The experimental evaluations of synthetic datasets corroborate the theoretical properties of our approach. For differentially private k-means clustering, we propose a convergent differentially private k-means algorithm that addresses the non-convergence problem of existing work. The key idea is that, at each iteration, we sample a centroid for the next iteration from a specially defined area in each cluster with a selected orientation to guarantee both convergence and convergence rates. Such an orientation is determined by either past centroid movements or past plus future centroid movements. Both mathematical and experimental evaluations show that, because of the convergence, our approach achieves enhanced clustering quality over the state-of-the-art of DP k-means clustering, while having the same DP guarantees.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202