SoK: Differential privacies

Desfontaines, Damien; Pejó, Balázs

doi:10.2478/popets-2020-0028

Cited by 67 publications

(49 citation statements)

References 154 publications

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“…Some more challenging extensions of this topic include the study of incremental anonymization of data sets published in a cloud environment [ 37 , 38 ] and handling a large amount of missing values in SRS data [ 39 ]. Recently, the emerging differential privacy [ 40 - 42 ] has been widely recognized as a more rigorous privacy protection method [ 43 ]. Our recent work [ 44 ] on integrating differential privacy to anonymize a single release of SRS data has shown promising results.…”

Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Wang¹,

Lin²

2021

JMIR Med Inform

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Background Spontaneous reporting systems (SRSs) have been increasingly established to collect adverse drug events for fostering adverse drug reaction (ADR) detection and analysis research. SRS data contain personal information, and so their publication requires data anonymization to prevent the disclosure of individuals’ privacy. We have previously proposed a privacy model called MS(k, θ*)-bounding and the associated MS-Anonymization algorithm to fulfill the anonymization of SRS data. In the real world, the SRS data usually are released periodically (eg, FDA Adverse Event Reporting System [FAERS]) to accommodate newly collected adverse drug events. Different anonymized releases of SRS data available to the attacker may thwart our single-release-focus method, that is, MS(k, θ*)-bounding. Objective We investigate the privacy threat caused by periodical releases of SRS data and propose anonymization methods to prevent the disclosure of personal privacy information while maintaining the utility of published data. Methods We identify potential attacks on periodical releases of SRS data, namely, BFL-attacks, mainly caused by follow-up cases. We present a new privacy model called PPMS(k, θ*)-bounding, and propose the associated PPMS-Anonymization algorithm and 2 improvements: PPMS+-Anonymization and PPMS++-Anonymization. Empirical evaluations were performed using 32 selected FAERS quarter data sets from 2004Q1 to 2011Q4. The performance of the proposed versions of PPMS-Anonymization was inspected against MS-Anonymization from some aspects, including data distortion, measured by normalized information loss; privacy risk of anonymized data, measured by dangerous identity ratio and dangerous sensitivity ratio; and data utility, measured by the bias of signal counting and strength (proportional reporting ratio). Results The best version of PPMS-Anonymization, PPMS++-Anonymization, achieves nearly the same quality as MS-Anonymization in both privacy protection and data utility. Overall, PPMS++-Anonymization ensures zero privacy risk on record and attribute linkage, and exhibits 51%-78% and 59%-82% improvements on information loss over PPMS+-Anonymization and PPMS-Anonymization, respectively, and significantly reduces the bias of ADR signal. Conclusions The proposed PPMS(k, θ*)-bounding model and PPMS-Anonymization algorithm are effective in anonymizing SRS data sets in the periodical data publishing scenario, preventing the series of releases from disclosing personal sensitive information caused by BFL-attacks while maintaining the data utility for ADR signal detection.

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Section: Discussionmentioning

confidence: 99%

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Wang¹,

Lin²

2021

JMIR Med Inform

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“…Only in these crossing points, we detail the use of all PPDL techniques. Accordingly, while Differential Privacy is a widespread technique in the field of PPDL [41][42][43][44][45][46], we only include proposals that intersect with PPCT (we refer to previous work for details on DP [47]). Also, Federated Learning (FL) is outside the scope of the paper, given its need for computing infrastructure and the reduced use of PPCT construction in the aggregation phase.…”

Section: Scope and Methodologymentioning

confidence: 99%

SoK: Privacy-Preserving Computation Techniques for Deep Learning

Cabrero-Holgueras

Pastrana

2021

Proceedings on Privacy Enhancing Technologies

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Deep Learning (DL) is a powerful solution for complex problems in many disciplines such as finance, medical research, or social sciences. Due to the high computational cost of DL algorithms, data scientists often rely upon Machine Learning as a Service (MLaaS) to outsource the computation onto third-party servers. However, outsourcing the computation raises privacy concerns when dealing with sensitive information, e.g., health or financial records. Also, privacy regulations like the European GDPR limit the collection, distribution, and use of such sensitive data. Recent advances in privacy-preserving computation techniques (i.e., Homomorphic Encryption and Secure Multiparty Computation) have enabled DL training and inference over protected data. However, these techniques are still immature and difficult to deploy in practical scenarios. In this work, we review the evolution of the adaptation of privacy-preserving computation techniques onto DL, to understand the gap between research proposals and practical applications. We highlight the relative advantages and disadvantages, considering aspects such as efficiency shortcomings, reproducibility issues due to the lack of standard tools and programming interfaces, or lack of integration with DL frameworks commonly used by the data science community.

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“…A significant amount of research focuses on improving the accuracy of query results while still maintaining differential privacy. In this work, for clarity, we keep the description of our system conceptually simple, and explicitly do not make use of techniques like smooth sensitivity [40], tight privacy budget computation methods [22,35], variants of the differential privacy definition [6,8,37], adjustment of noise levels to a prespecified set of queries [30], or generation of differentially private synthetic data to answer arbitrarily many queries afterwards [5,26,27].…”

Section: Related Workmentioning

confidence: 99%

Differentially Private SQL with Bounded User Contribution

Wilson¹,

Zhang²,

Lam³

et al. 2020

Proceedings on Privacy Enhancing Technologies

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Differential privacy (DP) provides formal guarantees that the output of a database query does not reveal too much information about any individual present in the database. While many differentially private algorithms have been proposed in the scientific literature, there are only a few end-to-end implementations of differentially private query engines. Crucially, existing systems assume that each individual is associated with at most one database record, which is unrealistic in practice. We propose a generic and scalable method to perform differentially private aggregations on databases, even when individuals can each be associated with arbitrarily many rows. We express this method as an operator in relational algebra, and implement it in an SQL engine. To validate this system, we test the utility of typical queries on industry benchmarks, and verify its correctness with a stochastic test framework we developed. We highlight the promises and pitfalls learned when deploying such a system in practice, and we publish its core components as open-source software.

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SoK: Differential privacies

Cited by 67 publications

References 154 publications

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

Privacy-Preserving Anonymity for Periodical Releases of Spontaneous Adverse Drug Event Reporting Data: Algorithm Development and Validation

SoK: Privacy-Preserving Computation Techniques for Deep Learning

Differentially Private SQL with Bounded User Contribution

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