Computer security -- ESORICS 2019 : 24th European Symposium on Research in Computer Security, Luxembourg, September 23-27, 2019, Proceedings. Part I / Kazue Sako, Steve Schneider, Peter Y.A. Ryan (eds.).

International conference proceedings.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed September 24, 2019).

Intro; Preface; Organization; Abstracts of Keynote Talks; The Insecurity of Machine Learning: Problems and Solutions; Electronic Voting: A Journey to Verifiability and Vote Privacy; Cryptocurrencies and Distributed Consensus: Hype and Science; Contents -- Part I; Contents -- Part II; Machine Learning; Privacy-Enhanced Machine Learning with Functional Encryption; 1 Introduction; 2 Functional Encryption Libraries; 2.1 Implemented Schemes; 3 Implementation of Cryptographic Primitives; 3.1 Pairing Schemes; 3.2 Lattice Schemes; 3.3 ABE Schemes; 4 Benchmarks; 4.1 Inner-Product Schemes

4.2 Decentralized Inner-Product Scheme4.3 Quadratic Scheme; 5 Privacy-Friendly Prediction of Cardiovascular Diseases; 6 London Underground Anonymous Heatmap; 7 Neural Networks on Encrypted MNIST Dataset; 8 Conclusions and Future Work; References; Towards Secure and Efficient Outsourcing of Machine Learning Classification; 1 Introduction; 2 Related Work; 3 Problem Statement; 3.1 Background on Decision Trees; 3.2 System Architecture; 3.3 Threat Model; 4 Design of Secure and Efficient Outsourcing of Decision Tree Based Classification; 4.1 Design Overview; 4.2 Protocol; 4.3 Security Guarantees

5 Experiments5.1 Setup; 5.2 Evaluation; 6 Conclusion; References; Confidential Boosting with Random Linear Classifiers for Outsourced User-Generated Data; 1 Introduction; 1.1 Scope of Work and Contributions; 2 Preliminary; 3 Framework; 3.1 SecureBoost Learning Protocol; 3.2 Security Model; 4 Construction with HE and GC; 4.1 Technical Detail; 5 Construction with SecSh and GC; 5.1 Technical Detail; 6 Cost Analysis; 7 Security Analysis; 7.1 Implication of Revealing It to CSP; 8 Experiments; 8.1 Effectiveness of RLC Boosting; 8.2 Cost Distribution; 8.3 Comparing with Other Methods

8.4 Effect of Releasing It9 Related Work; 10 Conclusion; A Appendix; A.1 Boosting Algorithm; A.2 Confidential Decision Stump Learning; A.3 Cloud and CSP Cost Breakdown and Scaling; References; BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks; 1 Introduction; 2 Preliminaries; 2.1 Supervised Machine Learning Model; 2.2 Model Extraction with only Labels; 3 Problem Definition; 3.1 Motivation and Threat Model; 3.2 Boundary-Sensitive Zone; 3.3 Boundary Differential Privacy; 4 Boundary Differentially Private Layer; 4.1 Identifying Sensitive Queries

4.2 Perturbation Algorithm: Boundary Randomized Response4.3 Summary; 5 Experiments; 5.1 Setup; 5.2 Overall Evaluation; 5.3 BDPL vs. Uniform Perturbation; 5.4 Impact of and; 6 Related Works; 7 Conclusion and Future Work; References; Information Leakage; The Leakage-Resilience Dilemma; 1 Introduction; 2 Randomization Granularity; 2.1 Virtual-Memory Randomization; 2.2 Physical-Memory Randomization; 3 Threat Model; 4 Relative ROP Attacks; 4.1 Partial Pointer Overwriting; 4.2 RelROP Chaining; 5 RelROP Prevalence Analysis; 5.1 Analysis-Tool Architecture; 5.2 Analysis of Real-World Binaries

The two volume set, LNCS 11735 and 11736, constitutes the proceedings of the 24th European Symposium on Research in Computer Security, ESORIC 2019, held in Luxembourg, in September 2019. The total of 67 full papers included in these proceedings was carefully reviewed and selected from 344 submissions. The papers were organized in topical sections named as follows: Part I: machine learning; information leakage; signatures and re-encryption; side channels; formal modelling and verification; attacks; secure protocols; useful tools; blockchain and smart contracts. Part II: software security; cryptographic protocols; security models; searchable encryption; privacy; key exchange protocols; and web security. -- Provided by publisher.