Privacy-preserving social network analysis

Data privacy in social networks is a growing concern that threatens to limit access to important information contained in these data structures. Analysis of the graph structure of social networks can provide valuable information for revenue generation and social science research, but unfortunately, ensuring this analysis does not violate individual privacy is difficult. Simply removing obvious identifiers from graphs or even releasing only aggregate results of analysis may not provide sufficient protection. Differential privacy is an alternative privacy model, popular in data-mining over tabular data, that uses noise to obscure individuals\u27 contributions to aggregate results and offers a strong mathematical guarantee that individuals\u27 presence in the data-set is hidden. Analyses that were previously vulnerable to identification of individuals and extraction of private data may be safely released under differential-privacy guarantees. However, existing adaptations of differential privacy to social network analysis are often complex and have considerable impact on the utility of the results, making it less likely that they will see widespread adoption in the social network analysis world. In fact, social scientists still often use the weakest form of privacy protection, simple anonymization, in their social network analysis publications. ^ We review the existing work in graph-privatization, including the two existing standards for adapting differential privacy to network data. We then proposecontributor-privacy and partition-privacy , novel standards for differential privacy over network data, and introduce simple, powerful private algorithms using these standards for common network analysis techniques that were infeasible to privatize under previous differential privacy standards. We also ensure that privatized social network analysis does not violate the level of rigor required in social science research, by proposing a method of determining statistical significance for paired samples under differential privacy using the Wilcoxon Signed-Rank Test, which is appropriate for non-normally distributed data. ^ Finally, we return to formally consider the case where differential privacy is not applied to data. Naive, deterministic approaches to privacy protection, including anonymization and aggregation of data, are often used in real world practice. De-anonymization research demonstrates that some naive approaches to privacy are highly vulnerable to reidentification attacks, and none of these approaches offer the robust guarantee of differential privacy. However, we propose that these methods fall across a range of protection: Some are better than others. In cases where adding noise to data is especially problematic, or acceptance and adoption of differential privacy is especially slow, it is critical to have a formal understanding of the alternatives. We define De Facto Privacy, a metric for comparing the relative privacy protection provided by deterministic approaches

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

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

Differential Privacy - A Balancing Act

Data privacy is an ever important aspect of data analyses. Historically, a plethora of privacy techniques have been introduced to protect data, but few have stood the test of time. From investigating the overlap between big data research, and security and privacy research, I have found that differential privacy presents itself as a promising defender of data privacy.Differential privacy is a rigorous, mathematical notion of privacy. Nevertheless, privacy comes at a cost. In order to achieve differential privacy, we need to introduce some form of inaccuracy (i.e. error) to our analyses. Hence, practitioners need to engage in a balancing act between accuracy and privacy when adopting differential privacy. As a consequence, understanding this accuracy/privacy trade-off is vital to being able to use differential privacy in real data analyses.In this thesis, I aim to bridge the gap between differential privacy in theory, and differential privacy in practice. Most notably, I aim to convey a better understanding of the accuracy/privacy trade-off, by 1) implementing tools to tweak accuracy/privacy in a real use case, 2) presenting a methodology for empirically predicting error, and 3) systematizing and analyzing known accuracy improvement techniques for differentially private algorithms. Additionally, I also put differential privacy into context by investigating how it can be applied in the automotive domain. Using the automotive domain as an example, I introduce the main challenges that constitutes the balancing act, and provide advice for moving forward

A Novel System for Confidential Medical Data Storage Using Chaskey Encryption and Blockchain Technology

يعد التخزين الآمن للمعلومات الطبية السرية أمرًا بالغ الأهمية لمنظمات الرعاية الصحية التي تسعى إلى حماية خصوصية المريض والامتثال للمتطلبات التنظيمية. في هذا البحث، نقدم نظامًا جديدًا للتخزين الآمن للبيانات الطبية باستخدام تقنية تشفير Chaskey و blockchain. يستخدم النظام تشفير Chaskey لضمان سرية وسلامة البيانات الطبية، وتكنولوجيا blockchain لتوفير حلول تخزين البيانات الطبية بحيث يكون قابل للتطوير ويتميز باللامركزية. يستخدم النظام أيضًا تقنيات Bflow للتجزئة ومنها التجزئة الرأسية لتعزيز قابلية التوسع وإدارة البيانات المخزنة. بالإضافة إلى ذلك، يستخدم النظام العقود الذكية لفرض سياسات التحكم في الوصول والتدابير الأمنية الأخرى. سنقدم وصف للنظام المقترح بالتفصيل ونقدم تحليلاً لخصائصه الأمنية والأداء. تظهر نتائجنا أن النظام يوفر حلاً آمنًا للغاية وقابل للتطوير لتخزين البيانات الطبية السرية، مع تطبيقات محتملة في مجموعة واسعة من إعدادات الرعاية الصحية.Secure storage of confidential medical information is critical to healthcare organizations seeking to protect patient's privacy and comply with regulatory requirements. This paper presents a new scheme for secure storage of medical data using Chaskey cryptography and blockchain technology. The system uses Chaskey encryption to ensure integrity and confidentiality of medical data, blockchain technology to provide a scalable and decentralized storage solution. The system also uses Bflow segmentation and vertical segmentation technologies to enhance scalability and manage the stored data. In addition, the system uses smart contracts to enforce access control policies and other security measures. The description of the system detailing and provide an analysis of its security and performance characteristics. The resulting images were tested against a number of important metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), bit error rate (BER), Signal-to-Noise Ratio (SNR), Normalization Correlation (NC) and Structural Similarity Index (SSIM). Our results showing that the system provides a highly secure and scalable solution for storing confidential medical data, with potential applications in a wide range of healthcare settings

Privacy and spectral analysis of social network randomization

Social networks are of significant importance in various application domains. Un- derstanding the general properties of real social networks has gained much attention due to the proliferation of networked data. Many applications of networks such as anonymous web browsing and data publishing require relationship anonymity due to the sensitive, stigmatizing, or confidential nature of the relationship. One general ap- proach for this problem is to randomize the edges in true networks, and only release the randomized networks for data analysis. Our research focuses on the development of randomization techniques such that the released networks can preserve data utility while preserving data privacy. Data privacy refers to the sensitive information in the network data. The released network data after a simple randomization could incur various disclosures including identity disclosure, link disclosure and attribute disclosure. Data utility refers to the information, features, and patterns contained in the network data. Many important features may not be preserved in the released network data after a simple randomiza- tion. In this dissertation, we develop advanced randomization techniques to better preserve data utility of the network data while still preserving data privacy. Specifi- cally we develop two advanced randomization strategies that can preserve the spectral properties of the network or can preserve the real features (e.g., modularity) of the network. We quantify to what extent various randomization techniques can protect data privacy when attackers use different attacks or have different background knowl- edge. To measure the data utility, we also develop a consistent spectral framework to measure the non-randomness (importance) of the edges, nodes, and the overall graph. Exploiting the spectral space of network topology, we further develop fraud detection techniques for various collaborative attacks in social networks. Extensive theoretical analysis and empirical evaluations are conducted to demonstrate the efficacy of our developed techniques

Data Protection in Big Data Analysis

"Big data" applications are collecting data from various aspects of our lives more and more every day. This fast transition has surpassed the development pace of data protection techniques and has resulted in innumerable data breaches and privacy violations. To prevent that, it is important to ensure the data is protected while at rest, in transit, in use, as well as during computation or dispersal. We investigate data protection issues in big data analysis in this thesis. We address a security or privacy concern in each phase of the data science pipeline. These phases are: i) data cleaning and preparation, ii) data management, iii) data modelling and analysis, and iv) data dissemination and visualization. In each of our contributions, we either address an existing problem and propose a resolving design (Chapters 2 and 4), or evaluate a current solution for a problem and analyze whether it meets the expected security/privacy goal (Chapters 3 and 5). Starting with privacy in data preparation, we investigate providing privacy in query analysis leveraging differential privacy techniques. We consider contextual outlier analysis and identify challenging queries that require releasing direct information about members of the dataset. We define a new sampling mechanism that allows releasing this information in a differentially private manner. Our second contribution is in the data modelling and analysis phase. We investigate the effect of data properties and application requirements on the successful implementation of privacy techniques. We in particular investigate the effects of data correlation on data protection guarantees of differential privacy. Our third contribution in this thesis is in the data management phase. The problem is to efficiently protecting the data that is outsourced to a database management system (DBMS) provider while still allowing join operation. We provide an encryption method to minimize the leakage and to guarantee confidentiality for the data efficiently. Our last contribution is in the data dissemination phase. We inspect the ownership/contract protection for the prediction models trained on the data. We evaluate the backdoor-based watermarking in deep neural networks which is an important and recent line of the work in model ownership/contract protection