Heterogeneous Data Management, Polystores, and Analytics for Healthcare: VLDB 2019 Workshops, Poly and DMAH, Los Angeles, CA, USA, August 30, 2019, Revised Selected Papers
暂无分享,去创建一个
Michael Stonebraker | Timothy Mattson | Fusheng Wang | Vijay Gadepally | Alevtina Dubovitskaya | Gang Luo | Yanhui Laing | M. Stonebraker | V. Gadepally | T. Mattson | Fusheng Wang | Alevtina Dubovitskaya | G. Luo | Yanhui Laing
[1] Craig Gentry,et al. Pinocchio: Nearly Practical Verifiable Computation , 2013, IEEE Symposium on Security and Privacy.
[2] Carsten Binnig,et al. BlockchainDB - Towards a Shared Database on Blockchains , 2019, SIGMOD Conference.
[3] Siu-Ming Yiu,et al. SDB: A Secure Query Processing System with Data Interoperability , 2015, Proc. VLDB Endow..
[4] Joan Feigenbaum,et al. Using Intel Software Guard Extensions for Efficient Two-Party Secure Function Evaluation , 2016, Financial Cryptography Workshops.
[5] Andrew Chi-Chih Yao,et al. Protocols for secure computations , 1982, FOCS 1982.
[6] Zachary G. Ives,et al. Adaptive query processing: Why, How, When, and What Next? , 2007, VLDB.
[7] Gang Chen,et al. Database Meets Deep Learning: Challenges and Opportunities , 2016, SGMD.
[8] Wolf-Tilo Balke,et al. Multi-objective Query Processing for Database Systems , 2004, VLDB.
[9] Martin Grund,et al. CPU and cache efficient management of memory-resident databases , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).
[10] Rajeev Motwani,et al. Two Can Keep A Secret: A Distributed Architecture for Secure Database Services , 2005, CIDR.
[11] Christoph Koch,et al. Multi-Objective Parametric Query Optimization , 2014, Proc. VLDB Endow..
[12] Matei Zaharia,et al. An Oblivious General-Purpose SQL Database for the Cloud , 2017, ArXiv.
[13] Mihir Bellare,et al. Efficient Garbling from a Fixed-Key Blockcipher , 2013, 2013 IEEE Symposium on Security and Privacy.
[14] Jonathan Lee,et al. Veritas: Shared Verifiable Databases and Tables in the Cloud , 2019, CIDR.
[15] Yuval Ishai,et al. Extending Oblivious Transfers Efficiently , 2003, CRYPTO.
[16] Sebastian Link,et al. Entity Integrity, Referential Integrity, and Query Optimization with Embedded Uniqueness Constraints , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).
[17] Omer Reingold,et al. Computational Differential Privacy , 2009, CRYPTO.
[18] Craig Gentry,et al. Quadratic Span Programs and Succinct NIZKs without PCPs , 2013, IACR Cryptol. ePrint Arch..
[19] Abel N. Kho,et al. SMCQL: Secure Query Processing for Private Data Networks , 2016, Proc. VLDB Endow..
[20] Toniann Pitassi,et al. The Limits of Two-Party Differential Privacy , 2010, 2010 IEEE 51st Annual Symposium on Foundations of Computer Science.
[21] Viktor Leis,et al. Compiling Database Queries into Machine Code , 2014, IEEE Data Eng. Bull..
[22] Jonathan Katz,et al. vSQL: Verifying Arbitrary SQL Queries over Dynamic Outsourced Databases , 2017, 2017 IEEE Symposium on Security and Privacy (SP).
[23] Michael Benedikt,et al. Querying with Access Patterns and Integrity Constraints , 2015, Proc. VLDB Endow..
[24] Senthil Nathan,et al. Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database , 2019, Proc. VLDB Endow..
[25] Donald D. Chamberlin,et al. Access Path Selection in a Relational Database Management System , 1989 .
[26] Irit Dinur,et al. Revealing information while preserving privacy , 2003, PODS.
[27] Siu-Ming Yiu,et al. Secure query processing with data interoperability in a cloud database environment , 2014, SIGMOD Conference.
[28] Frank Wang,et al. Splinter: Practical Private Queries on Public Data , 2017, NSDI.
[29] Volker Markl,et al. LEO: An autonomic query optimizer for DB2 , 2003, IBM Syst. J..
[30] Todd C. Mowry,et al. Relaxed Operator Fusion for In-Memory Databases: Making Compilation, Vectorization, and Prefetching Work Together At Last , 2017, Proc. VLDB Endow..
[31] Cynthia Dwork,et al. Differential Privacy , 2006, ICALP.
[32] Andrew Chi-Chih Yao,et al. How to Generate and Exchange Secrets (Extended Abstract) , 1986, FOCS.
[33] Marcel Keller,et al. Overdrive: Making SPDZ Great Again , 2018, IACR Cryptol. ePrint Arch..
[34] Dan Bogdanov,et al. Students and Taxes: a Privacy-Preserving Social Study Using Secure Computation , 2015, IACR Cryptol. ePrint Arch..
[35] Kartik Nayak,et al. Oblivious Data Structures , 2014, IACR Cryptol. ePrint Arch..
[36] Jonathan Katz,et al. Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation , 2017, CCS.
[37] Ashwin Machanavajjhala,et al. PrivateSQL: A Differentially Private SQL Query Engine , 2019, Proc. VLDB Endow..
[38] Lin Ma,et al. Self-Driving Database Management Systems , 2017, CIDR.
[39] Ramarathnam Venkatesan,et al. Secure database-as-a-service with Cipherbase , 2013, SIGMOD '13.
[40] Dan Bogdanov,et al. Sharemind: A Framework for Fast Privacy-Preserving Computations , 2008, ESORICS.
[41] Ashwin Machanavajjhala,et al. Architecting a Differentially Private SQL Engine , 2019, CIDR.
[42] Hari Balakrishnan,et al. CryptDB: protecting confidentiality with encrypted query processing , 2011, SOSP.
[43] Ivan Damgård,et al. Secure Multiparty Computation Goes Live , 2009, Financial Cryptography.
[44] Dawn Xiaodong Song,et al. Towards Practical Differential Privacy for SQL Queries , 2017, Proc. VLDB Endow..
[45] William Wallace,et al. KloakDB: A Data Federation for Analyzing Sensitive Data with K -anonymous Query Processing , 2019 .
[46] Chris Peikert,et al. ALCHEMY: A Language and Compiler for Homomorphic Encryption Made easY , 2018, CCS.
[47] Ashwin Machanavajjhala,et al. APEx: Accuracy-Aware Differentially Private Data Exploration , 2017, SIGMOD Conference.
[48] Magdalena Balazinska,et al. Learning State Representations for Query Optimization with Deep Reinforcement Learning , 2018, DEEM@SIGMOD.
[49] Somesh Jha,et al. Outis: Crypto-Assisted Differential Privacy on Untrusted Servers , 2019, ArXiv.
[50] Kartik Nayak,et al. ObliVM: A Programming Framework for Secure Computation , 2015, 2015 IEEE Symposium on Security and Privacy.
[51] Dan Boneh,et al. Callisto: A Cryptographic Approach to Detecting Serial Perpetrators of Sexual Misconduct , 2018, COMPASS.
[52] Olga Papaemmanouil,et al. Deep Reinforcement Learning for Join Order Enumeration , 2018, aiDM@SIGMOD.
[53] Craig Gentry,et al. Outsourcing Private RAM Computation , 2014, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science.
[54] Azer Bestavros,et al. Conclave: secure multi-party computation on big data , 2019, EuroSys.
[55] Samuel Madden,et al. Processing Analytical Queries over Encrypted Data , 2013, Proc. VLDB Endow..
[56] Ion Stoica,et al. Learning to Optimize Join Queries With Deep Reinforcement Learning , 2018, ArXiv.