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Oblivious Sampling Algorithms for Private Data Analysis [article]

Sajin Sasy, Olga Ohrimenko
2020 arXiv   pre-print
We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset.  ...  Experimentally we show that accuracy of models trained with shuffling and sampling is the same for differentially private models for MNIST and CIFAR-10, while sampling provides stronger privacy guarantees  ...  Acknowledgements The authors would like to thank anonymous reviewers for useful feedback that helped improve the paper.  ... 
arXiv:2009.13689v1 fatcat:lw2tgo5yhnbmhekhuwxd5wrosy

SAQE: Practical Privacy-Preserving Approximate Query Processing for Data Federations

Johes Bater, Yongjoo Park, Xi He, Xiao Wang, Jennie Rogers
2020 Proceedings of the VLDB Endowment  
As a result, private data federations are impractical for common database workloads.  ...  A private data federation enables clients to query the union of data from multiple data providers without revealing any extra private information to the client or any other data providers.  ...  In our first private sampling algorithm (i.e., an oblivious sampling algorithm), the data providers work together in secure computation to winnow down their input data to a set of samples for use in query  ... 
dblp:journals/pvldb/BaterP0WR20 fatcat:sfg3d4wkg5h2jfpwfhz272m364

An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors [article]

Joshua Allen, Bolin Ding, Janardhan Kulkarni, Harsha Nori, Olga Ohrimenko, Sergey Yekhanin
2019 arXiv   pre-print
Differential privacy has emerged as the main definition for private data analysis and machine learning.  ...  The algorithms we design in this framework show interesting interplay of ideas from the streaming algorithms, oblivious algorithms, and differential privacy.  ...  PROCHLO [10] , in particular, provides support for differential private data analysis.  ... 
arXiv:1807.00736v2 fatcat:dyoheafz2vhrvootoagmwa5xyi

Path Oblivious Heap: Optimal and Practical Oblivious Priority Queue

Elaine Shi
2020 2020 IEEE Symposium on Security and Privacy (SP)  
Our construction also implies a practical and optimal oblivious sorting algorithm which we call Path Oblivious Sort.  ...  Finally, we evaluate our algorithm for a multi-party computation scenario and show 7× to 8× reduction in the number of symmetric encryptions relative to the state of the art 1 .  ...  for oblivious algorithms.  ... 
doi:10.1109/sp40000.2020.00037 dblp:conf/sp/Shi20 fatcat:iif2l2dzyrc5zcxjmv6smpxw4i

Resource Oblivious Sorting on Multicores [chapter]

Richard Cole, Vijaya Ramachandran
2010 Lecture Notes in Computer Science  
The parallel complexity (or critical path length) of the algorithm is O(log n log log n), which improves on previous bounds for deterministic sample sort.  ...  Using PWS, we obtain a resource oblivious scheduling of our sorting algorithm that matches the performance of the processor-aware version. Our analysis includes the delay incurred by false-sharing.  ...  In Section 5, we return to the sorting algorithm, describing the details needed for a resource oblivious implementation, and this is followed by its analysis.  ... 
doi:10.1007/978-3-642-14165-2_20 fatcat:bkb3otgap5hmlh2kgymmiffcve

Resource Oblivious Sorting on Multicores

Richard Cole, Vijaya Ramachandran
2017 ACM Transactions on Parallel Computing  
The parallel complexity (or critical path length) of the algorithm is O( n · n), which improves on previous bounds for optimal cache oblivious sorting. The algorithm also has low false sharing costs.  ...  Finally, SPMS is resource oblivious in Athat the dependence on machine parameters appear only in the analysis of its performance, and not within the algorithm itself.  ...  In Section 5, we return to the sorting algorithm, describing the details needed for a resource oblivious implementation, and this is followed by its analysis.  ... 
doi:10.1145/3040221 fatcat:sqh4ozlzq5fq5eommeo6onrscy

MPC meets SNA: A Privacy Preserving Analysis of Distributed Sensitive Social Networks [article]

Varsha Bhat Kukkala, S.R.S Iyengar
2017 arXiv   pre-print
As a step towards realizing this, we design efficient data-oblivious algorithms for computing the K-shell decomposition and the PageRank centrality measure for a given DSSN.  ...  The designed data-oblivious algorithms can be translated into equivalent secure computation protocols.  ...  Design and Analysis of Data-Oblivious SNA Algorithms Computing the centrality measure of nodes is a popular network analysis technique for finding important nodes in a network.  ... 
arXiv:1705.06929v1 fatcat:mqdfhtte7fhd3jmikufoddfe3i

Path Oblivious Heap [article]

Elaine Shi
2019 IACR Cryptology ePrint Archive  
Our construction also implies a practical and optimal oblivious sorting algorithm which we call Path Oblivious Sort.  ...  More specificially, assuming roughly logarithmic client private storage, Path Oblivious Heap consumes 2× to 7× more bandwidth than the ordinary insecure binary heap; and Path Oblivious Sort consumes 4.5  ...  Acknowledgments The author would like to thank Kai-Min Chung for insightful technical discussions.  ... 
dblp:journals/iacr/Shi19 fatcat:p3ra5t5jyzbkdlfstl3ajlga2e

KloakDB: A Platform for Analyzing Sensitive Data with K-anonymous Query Processing [article]

Madhav Suresh, Zuohao She, William Wallace, Adel Lahlou, Jennie Rogers
2020 arXiv   pre-print
A private data federation enables data owners to pool their information for querying without disclosing their secret tuples to one another.  ...  Currently private data federations compute their queries fully-obliviously, guaranteeing that no information is revealed about the sensitive inputs of a data owner to their peers by observing the query's  ...  Private Data Federations A private data federation enables multiple, autonomous database systems to pool their sensitive data for analysis while keeping their input records private.  ... 
arXiv:1904.00411v2 fatcat:akwbhi7o6vb5zi3sq6rjxvch7q

OLIVE: Oblivious and Differentially Private Federated Learning on Trusted Execution Environment [article]

Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
2022 arXiv   pre-print
Thirdly, we propose oblivious yet efficient algorithms to prevent the memory access pattern leakage in OLIVE.  ...  Our main technical contributions are the analysis and countermeasures against the vulnerability of TEE in OLIVE.  ...  Oblivious algorithms [31, 65, 71] have been proposed to prevent memory access pattern leakage in private information retrieval [45] , private data analysis [4, 61] , and multi-party machine learning  ... 
arXiv:2202.07165v2 fatcat:a3uveafcqrdhzkulew27luv5hi

Brief announcement

Guy E. Blelloch, Phillip B. Gibbons, Harsha Vardhan Simhadri
2009 Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures - SPAA '09  
In this paper, we describe cache-oblivious sorting algorithms with optimal work, optimal cache complexity and polylogarithmic depth.  ...  Cache-oblivious algorithms have the advantage of achieving good sequential cache complexity across all levels of a multi-level cache hierarchy, regardless of the specifics (cache size and cache line size  ...  This work was funded in part by IBM, Intel, and the Microsoft-sponsored Center for Computational Thinking.  ... 
doi:10.1145/1583991.1584024 dblp:conf/spaa/BlellochGS09 fatcat:5dkojwqfvzba7binneyja2itzi

Prochlo

Andrea Bittau, Bernhard Seefeld, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes
2017 Proceedings of the 26th Symposium on Operating Systems Principles - SOSP '17  
., for application telemetry, error reporting, or demographic profiling.  ...  This paper describes a principled systems architecture---Encode, Shuffle, Analyze (ESA)---for performing such monitoring with high utility while also protecting user privacy.  ...  We thank the anonymous reviewers for their detailed feedback, and Martín Abadi, Johannes Gehrke, Lea Kissner, Noé Lutz, and Nicolas Papernot for their valuable advice on earlier drafts.  ... 
doi:10.1145/3132747.3132769 dblp:conf/sosp/BittauEMMRLRKTS17 fatcat:s5icmqnn6jgznjqhh4fo4fzahm

Exploring Differential Obliviousness

Amos Beimel, Kobbi Nissim, Mohammad Zaheri, Michael Wagner
2019 International Workshop on Approximation Algorithms for Combinatorial Optimization  
Chan et al. demonstrated that differential obliviousness allows achieving improved efficiency for several algorithmic tasks, including sorting, merging of sorted lists, and range query data structures.  ...  In this work, we continue the exploration of differential obliviousness, focusing on algorithms that do not necessarily examine all their input.  ...  showed that differential obliviousness does allow achieving improved efficiency for several algorithmic tasks, including sorting (over very small domains), merging of sorted lists, and range query data  ... 
doi:10.4230/lipics.approx-random.2019.65 dblp:conf/approx/BeimelNZ19 fatcat:krxpfa4ovnhmfce66rni25fo5i

Exploring Differential Obliviousness [article]

Amos Beimel, Kobbi Nissim, Mohammad Zaheri
2019 arXiv   pre-print
Chan et al. demonstrated that differential obliviousness allows achieving improved efficiency for several algorithmic tasks, including sorting, merging of sorted lists, and range query data structures.  ...  In this work, we continue the exploration and mapping of differential obliviousness, focusing on algorithms that do not necessarily examine all their input.  ...  First note that we only pay for privacy in the executions of algorithm SEARCH (reading and writing to the ORAM is perfectly private).  ... 
arXiv:1905.01373v2 fatcat:d2n3clr2uncwrhrb7cafxf7zta

Inherit Differential Privacy in Distributed Setting: Multiparty Randomized Function Computation [article]

Genqiang Wu and Yeping He and Jingzheng Wu and Xianyao Xia
2016 arXiv   pre-print
Importantly, all these protocols satisfy obliviousness and so can be proved to be secure in a simulation based manner.  ...  A composition theorem of differentially private protocols is also presented.  ...  Much works have been done in differentially private data analysis [32, 33, 34, 25, 35, 36, 37, 38, 39] . Our work tries to extend these algorithms to the distributed setting.  ... 
arXiv:1604.03001v1 fatcat:jg6piqufj5e2zendmvbqy5oase
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