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Coupled Relational Symbolic Execution for Differential Privacy [article]

Gian Pietro Farina, Stephen Chong, Marco Gaboardi
<span title="2020-07-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Here we use symbolic execution to support these tasks specifically for differential privacy.  ...  In this work we propose a technique based on symbolic execution for reasoning about differential privacy.  ...  Motivated by this picture, we propose a new technique based on relational symbolic execution, named Coupled Relational Symbolic Execution (CRSE), which supports proving and finding violation to differential  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.12987v1">arXiv:2007.12987v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/grwcsfz27ndadisixut42meg4a">fatcat:grwcsfz27ndadisixut42meg4a</a> </span>
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Coupled Relational Symbolic Execution for Differential Privacy [chapter]

Gian Pietro Farina, Stephen Chong, Marco Gaboardi
<span title="">2021</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
symbolic execution for reasoning about differential privacy.  ...  Here we use symbolic execution to support these tasks specifically for differential privacy.  ...  Acknowledgements We warmly thank the reviewers for helping us improving the paper. This work was supported by the National Science Foundation under Grant No. 1565365, 1565387 and 2040215.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-72019-3_8">doi:10.1007/978-3-030-72019-3_8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/heritsggnzbdnoeliyetibffbq">fatcat:heritsggnzbdnoeliyetibffbq</a> </span>
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Testing Differential Privacy with Dual Interpreters [article]

Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth
<span title="2020-10-08">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose here a fully automated framework for testing differential privacy, adapting a well-known "pointwise" technique from informal proofs of differential privacy.  ...  We also demonstrate how DPCheck can be deployed in a practical workflow to test differentially privacy for the 2020 US Census Disclosure Avoidance System (DAS).  ...  An apRHL judgment for differential privacy has the form ⊢ ∼ ( , ) : Φ ⇒ Ψ, where is a randomized program. 5 It describes a relation between the output distributions on related executions of .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.04126v1">arXiv:2010.04126v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gefyg52w6ffonamm7frwetwtau">fatcat:gefyg52w6ffonamm7frwetwtau</a> </span>
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Synthesizing coupling proofs of differential privacy

Aws Albarghouthi, Justin Hsu
<span title="2017-12-27">2017</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dqy7qc7jkzal5bz3gueys3siz4" style="color: black;">Proceedings of the ACM on Programming Languages</a> </i> &nbsp;
We present a push-button, automated technique for verifying ε-differential privacy of sophisticated randomized algorithms.  ...  (iv) Finally, we implement our technique in the FairSquare verifier and provide the first automated privacy proofs for a number of challenging algorithms from the differential privacy literature, including  ...  ACKNOWLEDGMENTS We thank Gilles Barthe, Marco Gaboardi, Zachary Kinkaid, Danfeng Zhang, and the anonymous reviewers for stimulating discussions and useful comments on earlier drafts of this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3158146">doi:10.1145/3158146</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/journals/pacmpl/AlbarghouthiH18.html">dblp:journals/pacmpl/AlbarghouthiH18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c3od4wpcbvhgfgaotao7crk5jm">fatcat:c3od4wpcbvhgfgaotao7crk5jm</a> </span>
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Differentially Private Collaborative Coupling Learning for Recommender Systems

Yanjun Zhang, Guangdong Bai, Mingyang Zhong, Xue Li, Ryan Ko
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/3vh5mzjvind2vk5qmq5bpyxhqa" style="color: black;">IEEE Intelligent Systems</a> </i> &nbsp;
Index Terms-Coupling learning, differential privacy, collaborative learning COUPLING LEARNING is an emerging research topic that refers to understanding, formalizing and quantifying the complex relations  ...  In this work, we develop a distributed collaborative coupling learning system which enables differential privacy.  ...  Finally, all of data owners execute descent on these noisy gradients and move forward to the next iteration (step 5). Symbol Explanation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mis.2020.3005930">doi:10.1109/mis.2020.3005930</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n6yr7wfidrdqvc27magihzokni">fatcat:n6yr7wfidrdqvc27magihzokni</a> </span>
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Automated Methods for Checking Differential Privacy [article]

Gilles Barthe, Rohit Chadha, Vishal Jagannath, A. Prasad Sistla and Mahesh Viswanathan
<span title="2019-10-09">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Thanks to its mathematical definition, differential privacy is also a natural target for formal analysis. A broad line of work uses logical methods for proving privacy.  ...  Differential privacy is a de facto standard for statistical computations over databases that contain private data.  ...  Formulas in L exp are built using variables {ǫ} ∪ {x i | i ∈ N}, constant symbols 0, 1, unary relation symbol e (·) applied only to the variable ǫ, binary function symbols +, −, ×, and binary relation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.04137v1">arXiv:1910.04137v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h2qddvpih5eylez7c2qv7zj2bq">fatcat:h2qddvpih5eylez7c2qv7zj2bq</a> </span>
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A Programming Framework for Differential Privacy with Accuracy Concentration Bounds [article]

Elisabet Lobo-Vesga Chalmers University of Technology
<span title="2019-09-10">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, most of the programming languages for differential privacy proposed so far provide support for reasoning about privacy but not for reasoning about the accuracy of data analyses.  ...  Differential privacy offers a formal framework for reasoning about privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing data analyses.  ...  privacy [51] , zero concentrated differential privacy [52] , or truncated concentrated differential privacy [53] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.07918v1">arXiv:1909.07918v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3kkbsxwi6bf4zhhez3nvedvmpu">fatcat:3kkbsxwi6bf4zhhez3nvedvmpu</a> </span>
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Probabilistic Couplings for Probabilistic Reasoning [article]

Justin Hsu
<span title="2017-11-01">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
optimal approximate coupling relating two subsets.  ...  proof of privacy for Sparse Vector in apRHL.  ...  differential privacy.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1710.09951v2">arXiv:1710.09951v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/emqjytis65go3l6nuzy2ivw52y">fatcat:emqjytis65go3l6nuzy2ivw52y</a> </span>
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Security and Privacy Issues for Connected Vehicles [article]

Wenjun Xiong, Robert Lagerström
<span title="2018-12-18">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To fill in the gap, this work present a privacy specification for vehicles based on vehicleLang, which uses the Meta Attack Language (MAL) to assess the security of connected vehicles in a formal way,  ...  with a special focus on the privacy aspect.  ...  Privacy MAL-specification for Vehicles In this section, we present a privacy MAL-specification for vehicles based on vehicleLang, and the simulation will be executed by the MAL compiler (to be done).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1812.04967v2">arXiv:1812.04967v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jg2lf6ezarcr3epjfou46m3ley">fatcat:jg2lf6ezarcr3epjfou46m3ley</a> </span>
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Contextual Linear Types for Differential Privacy [article]

Matías Toro, David Darais, Chike Abuah, Joe Near, Damián Árquez, Federico Olmedo, Éric Tanter
<span title="2021-11-12">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We formalize the core of the Jazz language, prove it sound for privacy via a logical relation for metric preservation, and illustrate its expressive power through a number of case studies drawn from the  ...  recent differential privacy literature.  ...  privacy: the maximum argument variation for each of two executions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.11342v2">arXiv:2010.11342v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gwgcsvx2mzewlpui56256g4noa">fatcat:gwgcsvx2mzewlpui56256g4noa</a> </span>
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Relational Symbolic Execution

Gian Pietro Farina, Stephen Chong, Marco Gaboardi
<span title="">2019</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/mht6wa2vdbg7dn6n6lws4hpzti" style="color: black;">Proceedings of the 21st International Symposium on Principles and Practice of Programming Languages 2019 - PPDP &#39;19</a> </i> &nbsp;
We design a relational symbolic execution engine, named RelSym which supports interactive refutation, as well as proving of relational properties for programs written in a language with arrays and for-like  ...  In this work we generalize symbolic execution to support program analysis for relational specifications in the form of relational properties -these are properties about two runs of two programs on related  ...  Conclusions In this work we presented RelSym, a foundational framework for relational symbolic execution.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3354166.3354175">doi:10.1145/3354166.3354175</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ppdp/FarinaCG19.html">dblp:conf/ppdp/FarinaCG19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c5cjfas7srfjfbwjx6l2mc42ou">fatcat:c5cjfas7srfjfbwjx6l2mc42ou</a> </span>
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Approximately Truthful Multi-Agent Optimization Using Cloud-Enforced Joint Differential Privacy [article]

M.T. Hale, M. Egerstedt
<span title="2016-11-08">2016</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This work extends our earlier work on incorporating ordinary differential privacy into multi-agent optimization, and we show that this work can be modified to provide a disincentivize for misreporting  ...  Numerical results are presented to demonstrate convergence of the optimization algorithm under joint differential privacy.  ...  Our work focuses on trajectory-level privacy for state trajectories that are dynamically generated as an optimization algorithm is executed.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1509.08161v3">arXiv:1509.08161v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fwopcwz2ynhmbmysfzskeve6pm">fatcat:fwopcwz2ynhmbmysfzskeve6pm</a> </span>
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Clustering Algorithm for Big Datasets with Mixed Attribute Features under Spark

Jiankai Wang, Gengxin Sun
<span title="2022-04-11">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
is proposed, and the differential privacy model is a privacy-preserving model for this problem [11] .  ...  implementing differential privacy, a Laplace mechanism that provides privacy protection for numerical results and an exponential mechanism that provides privacy protection for nonnumerical results [18  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/6156799">doi:10.1155/2022/6156799</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ekurwccwlrhn5d5ygzkxel6su4">fatcat:ekurwccwlrhn5d5ygzkxel6su4</a> </span>
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A Study on Privacy Preserving Methodologies in Big Data

J. Vinothkumar, V. Santhi
<span title="2016-12-30">2016</span> <i title="Indian Society for Education and Environment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wffwpj3q45g5zfjzfeyagk5uea" style="color: black;">Indian Journal of Science and Technology</a> </i> &nbsp;
Then, detailed and familiar confidentiality enhancing technologies for the big data are offered.  ...  The requirement to change the debate from big data and confidentiality to big data with confidentiality, implementing the confidentiality and data safeguarding schemes as a vital value of big data for  ...  Related outcomes have been attained for anonymization of information in locality-based services. Locality privacy via generalization and differential privacy is also implemented 53 .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2016/v9is1/104234">doi:10.17485/ijst/2016/v9is1/104234</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o3irtlp4hrcxbnuhufjczebw7q">fatcat:o3irtlp4hrcxbnuhufjczebw7q</a> </span>
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Behavior Decomposition: Aspect-Level Browser Extension Clustering and Its Security Implications [chapter]

Bin Zhao, Peng Liu
<span title="">2013</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We then build profiles of AEB clusters for both extensions and categories (of extensions) to detect suspicious extensions.  ...  However, large amount of extensions can be downloaded from webstores without sufficient trust or safety scrutiny, which keeps users from differentiating benign extensions from malicious ones.  ...  This work was supported by ARO W911NF-09-1-0525 (MURI), NSF CNS-0905131, NSF CNS-1223710, and ARO MURI project "Adversarial and Uncertain Reasoning for Adaptive Cyber Defense: Building the Scientific Foundation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-41284-4_13">doi:10.1007/978-3-642-41284-4_13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eh57zvu4cbec3e7w4vbzgamyqi">fatcat:eh57zvu4cbec3e7w4vbzgamyqi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170811215949/https://s2.ist.psu.edu/paper/RAID2013_Cameraready_Submission_35.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/61/6e/616ea2e7be5ba868bcebd5a33969e1095a250374.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-41284-4_13"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>
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