Application Of Differential Privacy Research Articles

Ethical Considerations for Artificial Intelligence Applications for HIV

Human Immunodeficiency Virus (HIV) is a stigmatizing disease that disproportionately affects African Americans and Latinos among people living with HIV (PLWH). Researchers are increasingly utilizing artificial intelligence (AI) to analyze large amounts of data such as social media data and electronic health records (EHR) for various HIV-related tasks, from prevention and surveillance to treatment and counseling. This paper explores the ethical considerations surrounding the use of AI for HIV with a focus on acceptability, trust, fairness, and transparency. To improve acceptability and trust towards AI systems for HIV, informed consent and a Federated Learning (FL) approach are suggested. In regard to unfairness, stakeholders should be wary of AI systems for HIV further stigmatizing or even being used as grounds to criminalize PLWH. To prevent criminalization, in particular, the application of differential privacy on HIV data generated by data linkage should be studied. Participatory design is crucial in designing the AI systems for HIV to be more transparent and inclusive. To this end, the formation of a data ethics committee and the construction of relevant frameworks and principles may need to be concurrently implemented. Lastly, the question of whether the amount of transparency beyond a certain threshold may overwhelm patients, thereby unexpectedly triggering negative consequences, is posed.

A data-driven approach to choosing privacy parameters for clinical trial data sharing under differential privacy.

Clinical trial data sharing is crucial for promoting transparency and collaborative efforts in medical research. Differential privacy (DP) is a formal statistical technique for anonymizing shared data that balances privacy of individual records and accuracy of replicated results through a "privacy budget" parameter, ε. DP is considered the state of the art in privacy-protected data publication and is underutilized in clinical trial data sharing. This study is focused on identifying ε values for the sharing of clinical trial data. We analyzed 2 clinical trial datasets with privacy budget ε ranging from 0.01 to 10. Smaller values of ε entail adding greater amounts of random noise, with better privacy as a result. Comparison of rates, odds ratios, means, and mean differences between the original clinical trial datasets and the empirical distribution of the DP estimator was performed. The DP rate closely approximated the original rate of 6.5% when ε > 1. The DP odds ratio closely aligned with the original odds ratio of 0.689 when ε ≥ 3. The DP mean closely approximated the original mean of 164.64 when ε ≥ 1. As ε increased to 5, both the minimum and maximum DP means converged toward the original mean. There is no consensus on how to choose the privacy budget ε. The definition of DP does not specify the required level of privacy, and there is no established formula for determining ε. Our findings suggest that the application of DP holds promise in the context of sharing clinical trial data.

Differential privacy in edge computing-based smart city Applications:Security issues, solutions and future directions

Fast-growing smart city applications, such as smart delivery, smart community, and smart health, are generating big data that are widely distributed on the internet. IoT (Internet of Things) systems are at the centre of smart city applications, as traditional cloud computing is insufficient for satisfying the critical requirements of smart IoT systems. Due to the nature of smart city applications, massive IoT data may contain sensitive information; hence, various privacy-preserving methods, such as anonymity, federated learning, and homomorphic encryption, have been utilised over the years. Furthermore, limited concern has been given to the resource consumption for data privacy-preserving in edge computing environments, which are resource-constrained when compared with cloud data centres. In particular, differential privacy (DP) has been an effective privacy-preserving method in the edge computing environment. However, there is no dedicated study on DP technology with a focus on smart city applications in the edge computing environment. To fill this gap, this paper provides a comprehensive study on DP in edge computing-based smart city applications, covering various aspects, such as privacy models, research methods, mechanisms, and applications. Our study focuses on five areas of data privacy, including data transmitting privacy, data processing privacy, data model training privacy, data publishing privacy, and location privacy. In addition, we investigate many potential applications of DP in smart city application scenarios. Finally, future directions of DP in edge computing are envisaged. We hope this study can be a useful roadmap for researchers and practitioners in edge computing enable smart city applications.

A Q&A about Differential Privacy

This article is a Q&A on differential privacy (DP), a state-of-the-art and popular privacy concept. There are 11 questions, including “What is DP?”, the reasons for its popularity among privacy researchers, examples of real-world applications of DP, and open-source code and platforms on DP. For those who have heard about DP, but have not yet had a chance to read papers and publications on DP (either technical or non-technical) I hope this Q&A will provide something useful and insightful.

Application of Differential Privacy to the 2020 U.S. Census and American Community Survey: Researchers’ Understanding and Reactions

The U.S. Census and the related American Community Survey (ACS) are used for studies of many types by researchers in a variety of domains. The U.S. Census Bureau discovered that the methods intended to preserve the privacy of individuals used for the 2010 survey were not adequate. Thus, a decision was made to apply differential privacy (DP) to the data from the 2020 Census. DP methods introduce noise into the data set with the intention of allowing the group statistics still to be useful, while protecting the individual data by way of random perturbation. Researchers expressed concern as to how much this application of DP will impact research. We report results of a survey conducted of researchers who published studies based on the 2010 U.S. Census data or the ACS data to assess their understanding of and concerns about DP.

Investigation and Application of Differential Privacy in Bitcoin

Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.

Novel algorithm for efficient privacy preservation in data analytics

Objective: To address the modern privacy threats in data analytics by designing an efficient privacy preserving data analytics technique. Methods: The method applied is a non anonymized method that uses the concepts of synthesizing quasi identifiers and application of differential privacy. The proposed method was applied to three data sets viz. Adult data set, Statlogdata set and Indian Liver Patient data set. All the data sets are freely available in the UCI repository. Findings: The study presents “Synthesize Quasi Identifiers and apply Differential Privacy” (SQIDP) which is proved to be a more efficient and scalable algorithm. Compared to anonymity based algorithms SQIDP is not prone to similarity attacks, background knowledge attacks, attribute disclosure, and inference attacks. Anonymization, cryptographic, SWARM, and randomization methods will reduce data utility whereas SQIDP offers 100% data utility. Hence it is more efficient than other techniques. SQIDP was applied on three different data sets with 270, 583, and 48842 records but the execution time of the algorithm remained the same for all three data sets. SQIDP is proved to be a better privacy preservation technique with 100% data utility because it is not anonymized that abides by the recommendation in many privacy legislations like GDPR (General Data Protection Regulation) of the European Union and PDP (Personal Data Protection bill) of India. Keywords: Data privacy; privacy regulations; privacy preservation; synthetic data; differential

O conceito de privacidade diferencial em relação à reidentificação de dados pessoais

O presente artigo tem por objetivo pesquisar uma das lacunas encontradas na Lei Geral de Proteção de Dados Pessoais (Lei nº 13.709/2018), que — se mal implementada em seu programa de conformidade — pode expor os dados de clientes e usuários. O problema de pesquisa se resume ao seguinte questionamento: há fragilidade na proteção de dados do cidadão, em razão dos mecanismos adotados pela legislação Brasileira? Num primeiro momento, tratou-se, brevemente, da anonimização de dados pessoais face à reidenticação, a fim de se poder tratar, posteriormente, da aplicação da privacidade diferencial. O método utilizado na abordagem foi o descritivo-sistemático. O método de interpretação jurídica foi o tópico sistemático. Verificou-se que é imperioso repensar os conceitos sobre segurança da informação de forma a transcender a mera obrigação legal, motivando de igual forma a inovação, a criatividade e a responsabilidade no tratamento de dados pessoais. Conclui-se, em linhas gerais, que a noção de segurança e de sigilo deve permear todas as atividades do tratamento de dados pessoais, desde a concepção de um produto ou serviço.

Employing differential privacy for big data security

Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user’s privacy becomes a challenge. Lots of researches had been carried out on securing big data, but still limited in efficient privacy management and data sensitivity. This paper designs a mechanism that employs Differential Privacy for protection of personal data, which enforces privacy and access restriction level. The Differential Privacy technique acts on the request by introducing a minimum distortion to the information provided by the database system. The mechanism, DP-Data was implemented with Python scripting and Java Programming languages, Mysql and VmWare on Apache Hadoop platform. To test the effectiveness of DP-Data, a medical dataset with 1,048,576 instances and 12 attributes was employed. It was evaluated based on its utility, scalability, accuracy, sensitivity, specificity and processing time. The results indicated accuracy of 95.80 %, sensitivity of 93.60 %, specificity of 98.00 % and 0.40 ms processing time with high utility and good scalability which shows that the time it takes to preserve a data of 5000 tuples or less are almost similar. From these results, the application of differential privacy in solving privacy issue proved a high level of efficiency. Hence, the deployment of a secure big data framework that is based on access restriction and preserved level of privacy poses a higher level of protection of user’s privacy in comparison with other techniques.Keywords: Big Data, Privacy Preservation, Differential Privacy

Quantitative Relationship Between Localization Accuracy and Location Privacy Level in Wireless Localization System

In wireless localization systems, location information with respect to anchors will be exposed to untrusted target or third party inevitably leading to location privacy leakage. Differential privacy based techniques can provide theoretical guarantee to privacy preservation, while such privacy preservation will degrade localization accuracy. We note that the quantitative relationship between localization accuracy and privacy level is still unclear. In this paper, we derive the Cramer-Rao lower bound (CRLB) about the target location when the anchors take a privacy preservation mechanism satisfying geo-indistinguishability, which is an application of differential privacy on Euclidean metric. The closed-form relationship between the target localization accuracy and the anchors’ location privacy level is provided respectively for range-only and bearing-only localization. Numerical results in further verify our theoretical results.

The Differential Privacy Corner: What has the US Backed Itself Into?

An expanding body of data privacy research reveals that computational advances and ever-growing amounts of publicly retrievable data increase re-identification risks. Because of this, data publishers are realizing that traditional statistical disclosure limitation methods may not protect privacy.
 This paper discusses the use of differential privacy at the US Census Bureau to protect the published results of the 2020 census. We first discuss the legal framework under which the Census Bureau intends to use differential privacy. The Census Act in the US states that the agency must keep information confidential, avoiding “any publication whereby the data furnished by any particular establishment or individual under this title can be identified.” The fact that Census may release fewer statistics in 2020 than in 2010 is leading scholars to parse the meaning of identification and reevaluate the agency’s responsibility to balance data utility with privacy protection.
 We then describe technical aspects of the application of differential privacy in the U.S. Census. This data collection is enormously complex and serves a wide variety of users and uses -- 7.8 billion statistics were released using the 2010 US Census. This complexity strains the application of differential privacy to ensure appropriate geographic relationships, respect legal requirements for certain statistics to be free of noise infusion, and provide information for detailed demographic groups.
 We end by discussing the prospects of applying formal mathematical privacy to other information products at the Census Bureau. At present, techniques exist for applying differential privacy to descriptive statistics, histograms, and counts, but are less developed for more complex data releases including panel data, linked data, and vast person-level datasets. We expect the continued development of formally private methods to occur alongside discussions of what privacy means and the policy issues involved in trading off protection for accuracy.

Administrative Data Censuses in US States

This paper describes work underway to conduct administrative data censuses in several US states. We are helping states identify sources that, when combined, reflect their entire population, and introduce approaches to efficiently and routinely integrate their data.
 The national 2020 Census of Population and Housing faces numerous challenges. Operationally, there are concerns about self-response, inadequate technology integration and testing, hacking and data manipulation of this first online census, and the application of differential privacy to published results. This project’s main aim is to support population benchmarking in states to ensure correct counts affecting federal funding and political representation. This capacity-building has spillover effects for states: it can support their program administration, emergency preparedness, and policymaking.
 We describe our plan and results to date. We have developed governance approaches and templates and proposed schema to de-duplicate individuals across data sources. We established a secure project workspace designed for cross-institution collaboration to design approaches for states to clean and standardize identifiers, to link across and de-duplicate files, to determine or impute relationships, and to resolve discrepancies observed between sources on residence and demographic characteristics.
 We have assessed operational readiness in the states and gathered the concerns and recommendations of state integrated data system owners, demographers, advocacy groups, and program administrators. This stakeholder engagement was critical to design workflows and planned products. We need to report on the coverage, completeness, and overlap of data sources, and of the linked data. Next we will work with states to determine what population measures they need from this new capacity: what demographic characteristics and levels of geography are desired for tables and maps.
 The project aims to build trust in the data and process from the ground up. We will develop training materials and offer technical assistance to help other states adopt this population benchmarking capacity.

Generalized Gaussian Mechanism for Differential Privacy

Assessment of disclosure risk is of paramount importance in the research and applications of data privacy techniques. The concept of differential privacy (DP) formalizes privacy in probabilistic terms and provides a robust concept for privacy protection without making assumptions about the background knowledge of adversaries. Practical applications of DP involve development of DP mechanisms to release results at a pre-specified privacy budget. In this paper, we generalize the widely used Laplace mechanism to the family of generalized Gaussian (GG) mechanism based on the $l_p$ global sensitivity of statistical queries. We explore the theoretical requirement for the GG mechanism to reach DP at prespecified privacy parameters, and investigate the connections and differences between the GG mechanism and the Exponential mechanism based on the GG distribution We also present a lower bound on the scale parameter of the Gaussian mechanism of $(\epsilon,\delta)$-probabilistic DP as a special case of the GG mechanism, and compare the statistical utility of the sanitized results in the tail probability and dispersion in the Gaussian and Laplace mechanisms. Lastly, we apply the GG mechanism in 3 experiments (the mildew, Czech, adult data), and compare the accuracy of sanitized results via the $l_1$ distance and Kullback-Leibler divergence and examine how sanitization affects the prediction power of a classifier constructed with the sanitized data in the adult experiment.

A Survey on Differential Privacy and Applications

Privacy preserving in data release and mining is a hot topic in the information security field currently. As a new privacy notion, differential privacy (DP) has grown in popularity recently due to its rigid and provable privacy guarantee. After analyzing the advantage of differential privacy model relative to the traditional ones, this paper surveys the theory of differential privacy and its application on two aspects, privacy preserving data release (PPDR) and privacy preserving data mining (PPDM). In PPDR, we introduce the DP-based data release methodologies in interactive/non-interactive settings and compare them in terms of accuracy and sample complexity. In PPDM, we mainly summarize the implementation of DP in various data mining algorithms with interface-based/fully access-based modes as well as evaluating the performance of the algorithms. We finally review other applications of DP in various fields and discuss the future research directions.