000914766 000__ 05281cam\a2200589Ii\4500
000914766 001__ 914766
000914766 005__ 20230306150541.0
000914766 006__ m\\\\\o\\d\\\\\\\\
000914766 007__ cr\cn\nnnunnun
000914766 008__ 190924s2019\\\\sz\a\\\\o\\\\\101\0\eng\d
000914766 020__ $$a9783030299590$$q(electronic book)
000914766 020__ $$a3030299597$$q(electronic book)
000914766 020__ $$z9783030299583
000914766 0247_ $$a10.1007/978-3-030-29959-0$$2doi
000914766 035__ $$aSP(OCoLC)on1120754436
000914766 035__ $$aSP(OCoLC)1120754436
000914766 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dUKMGB$$dOCLCF$$dEBLCP
000914766 049__ $$aISEA
000914766 050_4 $$aQA76.9.A25
000914766 08204 $$a005.8$$223
000914766 1112_ $$aEuropean Symposium on Research in Computer Security$$n(24th :$$d2019 :$$cLuxembourg)
000914766 24510 $$aComputer security -- ESORICS 2019 :$$b24th European Symposium on Research in Computer Security, Luxembourg, September 23-27, 2019, Proceedings.$$nPart I /$$cKazue Sako, Steve Schneider, Peter Y. A. Ryan (eds.).
000914766 2463_ $$aESORICS 2019
000914766 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000914766 300__ $$a1 online resource (xxv, 811 pages) :$$billustrations.
000914766 336__ $$atext$$btxt$$2rdacontent
000914766 337__ $$acomputer$$bc$$2rdamedia
000914766 338__ $$aonline resource$$bcr$$2rdacarrier
000914766 4901_ $$aLecture notes in computer science ;$$v11735
000914766 4901_ $$aLNCS sublibrary. SL 4, Security and cryptology
000914766 500__ $$aInternational conference proceedings.
000914766 500__ $$aIncludes author index.
000914766 5050_ $$aIntro; 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
000914766 5058_ $$a4.2 Decentralized Inner-Product Scheme4.3 Quadratic Scheme; 5 Privacy-Friendly Prediction of Cardiovascular Diseases; 6 London Underground Anonymous Heatmap; 7 Neural Networks on Encrypted MNIST Dataset; 8 Conclusions and Future Work; References; Towards Secure and Efficient Outsourcing of Machine Learning Classification; 1 Introduction; 2 Related Work; 3 Problem Statement; 3.1 Background on Decision Trees; 3.2 System Architecture; 3.3 Threat Model; 4 Design of Secure and Efficient Outsourcing of Decision Tree Based Classification; 4.1 Design Overview; 4.2 Protocol; 4.3 Security Guarantees
000914766 5058_ $$a5 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
000914766 5058_ $$a8.4 Effect of Releasing It9 Related Work; 10 Conclusion; A Appendix; A.1 Boosting Algorithm; A.2 Confidential Decision Stump Learning; A.3 Cloud and CSP Cost Breakdown and Scaling; References; BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks; 1 Introduction; 2 Preliminaries; 2.1 Supervised Machine Learning Model; 2.2 Model Extraction with only Labels; 3 Problem Definition; 3.1 Motivation and Threat Model; 3.2 Boundary-Sensitive Zone; 3.3 Boundary Differential Privacy; 4 Boundary Differentially Private Layer; 4.1 Identifying Sensitive Queries
000914766 5058_ $$a4.2 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
000914766 506__ $$aAccess limited to authorized users.
000914766 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 24, 2019).
000914766 650_0 $$aComputer security$$vCongresses.
000914766 650_0 $$aData encryption (Computer science)$$vCongresses.
000914766 650_0 $$aComputer networks$$xSecurity measures$$vCongresses.
000914766 650_0 $$aData protection$$vCongresses.
000914766 7001_ $$aSako, Kazue$$c(Innovation Producer),$$eeditor.
000914766 7001_ $$aSchneider, S. A.$$q(Steve A.),$$eeditor.
000914766 7001_ $$aRyan, Peter,$$d1957-$$eeditor.
000914766 830_0 $$aLecture notes in computer science ;$$v11735.
000914766 830_0 $$aLNCS sublibrary.$$nSL 4,$$pSecurity and cryptology.
000914766 852__ $$bebk
000914766 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-29959-0$$zOnline Access$$91397441.1
000914766 909CO $$ooai:library.usi.edu:914766$$pGLOBAL_SET
000914766 980__ $$aEBOOK
000914766 980__ $$aBIB
000914766 982__ $$aEbook
000914766 983__ $$aOnline
000914766 994__ $$a92$$bISE