Search Results - ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.0: General/D.2.0.0: Protection technical

CALENDAR OF EVENTS.

Source: Communications of the ACM; Apr2002, Vol. 45 Issue 4, p123-127, 5p

Subject Terms: CONFERENCES & conventions, COMPUTER science, SOFTWARE protection, MOLECULAR biology

Company/Entity: ASSOCIATION for Computing Machinery

Reviews & Products: COMMUNICATIONS of the ACM (Periodical)

A Model-based approach to realize privacy and data protection by design

Authors: Pedroza, Gabriel Muntes-Mulero, Victor Martin, Yod, Samuel et al.

Contributors: Pedroza, Gabriel Muntes-Mulero, Victor Martin, Yod, Samuel et al.

Source: 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)
IWPE'21 - 2021 International Workshop on Privacy Engineering
https://cea.hal.science/cea-03416657
IWPE'21 - 2021 International Workshop on Privacy Engineering, Sep 2021, vienne (Virtual conference), Austria

Subject Terms: Privacy by design, GDPR, data protection, model-based, personal data detection, DFD, MDE, MBSE, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques/D.2.2.0: Computer-aided software engineering (CASE), ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.10: Design/D.2.10.0: Methodologies, ACM: I.: Computing Methodologies/I.6: SIMULATION AND MODELING, [INFO.INFO-CE]Computer Science [cs]/Computational Engineering, Finance, and Science [cs.CE]

Subject Geographic: Austria

Time: vienne (Virtual conference), Austria

Relation: info:eu-repo/grantAgreement//787034/EU/Methods and tools for GDPR compliance through Privacy and Data Protection Engineering Programme: H2020-EU.3.7.6./PDP4E

Availability: https://cea.hal.science/cea-03416657
https://cea.hal.science/cea-03416657v1/document
https://cea.hal.science/cea-03416657v1/file/Pedroza_IWPEpaper_2021.pdf

Semantic Parameterization: A Process for Modeling Domain Descriptions.

Authors: BREAUX, TRAVIS D. ANTÓN, ANNIE I. DOYLE, JON

Source: ACM Transactions on Software Engineering & Methodology; Oct2008, Vol. 18 Issue 2, p5:1-5:27, 27p, 14 Charts

Subject Terms: PROGRAMMING language semantics, MAGNETIC domain, FERROMAGNETIC materials, NATURAL language processing, TECHNICAL specifications, QUERYING (Computer science), SOFTWARE engineering

A First Look at the General Data Protection Regulation (GDPR) in Open-Source Software.

Authors: Franke, Lucas Liang, Huayu Brantly, Aaron et al.

Source: ICSE: International Conference on Software Engineering; 2024, p268-269, 2p

Subject Terms: GENERAL Data Protection Regulation, 2016, OPEN source software, SOFTWARE engineering, COMPUTER software development, DATA privacy

1982 ACM Annual ComDUter Science Conference.

Source: Communications of the ACM; Jan1982, Vol. 25 Issue 1, p79-79, 1p

Subject Terms: CONFERENCES & conventions, COMPUTER science, COMPUTER software, UNIVERSITIES & colleges

People: DODD, George, GAFFNEY, John

Understanding the GDPR from a requirements engineering perspective—a systematic mapping study on regulatory data protection requirements.

Authors: Negri-Ribalta, Claudia Lombard-Platet, Marius Salinesi, Camille

Source: Requirements Engineering; Dec2024, Vol. 29 Issue 4, p523-549, 27p

Subject Terms: GENERAL Data Protection Regulation, 2016, DATA protection, REQUIREMENTS engineering, COMPUTER software development, REGULATORY compliance

Обфускаційні методи захисту програмного коду в комп’ютерних системах ; Obfuscation methods of software code protection in computer systems

Authors: Карплюк, Володимир Ігорович Karpliuk, Volodymyr Ясній, Олег Петрович et al.

Contributors: Карплюк, Володимир Ігорович Karpliuk, Volodymyr Ясній, Олег Петрович et al.

Subject Terms: комп’ютерна інженерія, обфускація, заплутування, JavaScript, надлишковість, методи обфускації, ПЗ, obfuscation, software, redundancy, obfuscation methods, 004.4

Subject Geographic: Тернопільський національний технічний університет імені Івана Пулюя, UA

Time: 123

Relation: 1. Collberg C. Taxonomy of Obfuscating Transformations / C. Collberg, C. Thomborson, D. Low. // Technical Report 148: Univ. of Auckland. – 1997. 2. Collberg C. A taxonomy of obfuscating transformations / C. Collberg, C. Thomborson, D. Low. – Department of Computer Science, The University of Auckland, New Zealand, 1997. – 36 p. 3. Ilsun Y. Malware obfuscation techniques: A brief survey / Y. Ilsun, Y. Kangbin. – Broadband: Wireless Computing, Communication and Applications (BWCCA), 2010. – (Department of Computer Science, The University of Auckland). 4. Schrittwieser S. Protecting software through obfuscation: can it keep pace with progress in code analysis [Електронний ресурс] / S. Schrittwieser, S. Katzenbeisser, J. Kinder // ACM Comput Surv. – 2016. – Режим доступу до ресурсу: https://doi.org/10.1145/2886012. 5. Modern obfuscation methods for secure coding / [I. Stepanenko, V. Kinzeryavyy, A. Nagi та ін.]. // Ukrainian Scientific Journal of Information Security. – 2016. – No22. – P. 32–37. 6. Xu H. Layered obfuscation: a taxonomy of software obfuscation techniques for layered security [Електронний ресурс] / H. Xu, Y. Zhou, J. Ming // Cybersecur 3. – 2020. – Режим доступу до ресурсу: https://doi.org/10.1186/s42400-020- 00049-3. 7. Cappaert J. A general model for hiding control flow [Електронний ресурс] / J. Cappaert, B. Preneel // ACM Workshop on Digital Rights Management. – 2010. – Режим доступу до ресурсу: https://doi.org/10.1145/1866870.1866877. 8. Barak B. Hopes, fears, and software obfuscation [Електронний ресурс] / B. Barak // Commun ACM. – 2016. – Режим доступу до ресурсу: https://doi.org/10.1145/2757276. 9. Barak B. On the (im) possibility of obfuscating programs [Електронний ресурс] / B. Barak, O. Goldreich, R. Impagliazzo // Annual International Cryptology79 Conference. – 2001. – Режим доступу до ресурсу: https://doi.org/10.1007/3-540- 44647-8_1. 10. Barrantes E. G. Randomized instruction set emulation [Електронний ресурс] / E. G. Barrantes, D. H. Ackley, S. Forrest // ACM Trans Inf Syst Secur. – 2005. – Режим доступу до ресурсу: https://doi.org/10.1145/1053283.1053286. 11. Barrington D. A. Bounded-width polynomial-size branching programs recognize exactly those languages [Електронний ресурс] / D. A. Barrington // NC1 In: STOC. – 1986. – Режим доступу до ресурсу: https://doi.org/10.1145/12130.12131. 12. Варновский Н. П. О применении методов деобфускации программ для обнаружения сложных компьютерных вирусов / Н. П. Варновский, В. А. Захаров, Н. Н. Кузюрин. // Известия Таганрогского радиотехнического университета. – 2006. – No6. – С. 18–20. 13. Bichsel B. Statistical deobfuscation of android applications [Електронний ресурс] / B. Bichsel, V. Raychev, P. Tsankov // CCS. – 2016. – Режим доступу до ресурсу: https://doi.org/10.1145/2976749.2978422. 14. Cheng X. Dynopvm: Vm-based software obfuscation with dynamic opcode mapping [Електронний ресурс] / X. Cheng, Y. Lin, D. Gao // International Conference on Applied Cryptography and Network Security. – 2019. – Режим доступу до ресурсу: https://doi.org/10.1007/978-3-030-21568-2_8. 15. Collberg C. Toward digital asset protection [Електронний ресурс] / C. Collberg, J. Davidson, R. Giacobazzi // IEEE Intell. – 2011. – Режим доступу до ресурсу: https://doi.org/10.1109/mis.2011.106. 16. Crane S. Thwarting cache side-channel attacks through dynamic software diversity [Електронний ресурс] / S. Crane, A. Homescu, S. Brunthaler // NDSS. – 2015. – Режим доступу до ресурсу: https://doi.org/10.14722/ndss.2015.23264. 17. Crane S. J. It’s a TRaP: table randomization and protection against function- reuse attacks [Електронний ресурс] / S. J. Crane, S. Volckaert, F. Schuster // CCS. – 2015. – Режим доступу до ресурсу: https://doi.org/10.1145/2810103.2813682.80 18. Kovacheva A. Efficient code obfuscation for android [Електронний ресурс] / A. Kovacheva // International Conference on Advances in Information Technology. – 2013. – Режим доступу до ресурсу: https://doi.org/10.1007/978-3- 319-03783-7_10. 19. Kuang K. Enhance virtual-machine-based code obfuscation security through dynamic bytecode scheduling [Електронний ресурс] / K. Kuang, Z. Tang, X. Gong // Comput Secur. – 2018. – Режим доступу до ресурсу: https://doi.org/10.1016/j.cose.2018.01.008. 20. Larsen P. Sok: Automated software diversity [Електронний ресурс] / P. Larsen, A. Homescu, S. Brunthaler // IEEE Symposium on Security and Privacy. – 2014. – Режим доступу до ресурсу: https://doi.org/10.1109/sp.2014.25. 21. Варновский Н. П. Современное состояние исследований в области обфускации программ: определения стойкости обфускации / Н. П. Варновский, В. А. Захаров, Н. Н. Кузюрин. // Труды Института системного программирования РАН. – 2013. – No26. – С. 167–198. 22. Madou M. On the effectiveness of source code transformations for binary obfuscation / M. Madou, B. Anckaert, B. de Sutter. // Proceedings of the International Conference on Software Engineering Research and Practice. – 2006. 23. Madou M. Software protection through dynamic code mutation / M. Madou, B. Anckaert, P. Moseley. // Proceedings of the 6-th international conference on Information Security Applications. – 2006. – P. 194–206. 24. Della Preda M. Semantic-based code obfuscation by abstract interpretation / M. Della Preda, G. Giacobazzi. // Journal of Computer Security. – 2009. – No17. – P. 855–908. 25. Della Preda M. Modelling Metamorphism by Abstract Interpretation / M. Della Preda, G. Giacobazzi, S. Debray. // Proceedings of the 17th International Static Analysis Symposium (SAS’10). Lecture Notes in Computer Science. – 2010. – No6337. – P. 218–235.81 26. Карплюк В. Захист програмного забезпечення на апаратному та програмному рівнях / В. Карплюк, О. Ясній // Матеріали VIII науково- технічної конференції ТНТУ ім. І. Пулюя, 9-10 грудня 2020 року — Т. : ТНТУ, 2020 — С. 105. — (Секція: Комп’ютерні системи та мережі). 27. Карплюк В. Методи обфускації програмного коду в комп’ютерних системах / В. Карплюк, О. Ясній // Матеріали IX наукової конференції ТНТУ ім. І. Пулюя, 25-26 листопада 2020 року — Т. : ТНТУ, 2020 — С. 77. — (Секція: Комп’ютерні системи та мережі). 28. Курмангалеев Ш. Ф. Описание подхода к разработке обфусцирующего компилятора / Ш. Ф. Курмангалеев, В. П. Корчагин, Р. А. Матевосян. // Труды Института системного программирования РАН. – 2012. – No23. – С. 67–76. 29. Методичні вказівки до виконання підрозділу "Охорона праці" в кваліфікаційних роботах магістрів спеціальності 123 «Комп’ютерна інженерія» /Укл.: Осухівська Г.М. – Тернопіль: ТНТУ імені Івана Пулюя, 2020. – 22 с. 30. Тишко Н.І. Розробка інтерактивного середовища соціалізації мовою програмування Java Script, фреймворк Meteor JS : автореферат дипломної роботи магістра за спеціальністю «121 — інженерія програмного забезпечення»/ Н.І. Тишко. — Тернопіль: ТНТУ, 2019. 31. Радчук М. І. Розробка веб-застосунку для візуалізації та аналізу мережі друзів за допомогою графів : дипломна робота магістра за спеціальністю «122 — комп’ютерні науки» / М. І. Радчук. — Тернопіль: ТНТУ, 2020. — 146 с. 32. Радчук В. Огляд методу обфускації коду, як протидія дизасемблюванню / В. Радчук, Н. Шингера // Матеріали XVIII наукової конференції ТНТУ ім. І. Пулюя, 29-30 жовтня 2014 року — Т. : ТНТУ, 2014 — С. 65-66. — (Секція: Інформаційні технології).; http://elartu.tntu.edu.ua/handle/lib/33269

Availability: http://elartu.tntu.edu.ua/handle/lib/33269

Watermarking, Tamper-Proofing, and Obfuscation -- Tools for Software Protection.

Authors: Collberg, Christian S. Thomborson, Clark

Source: IEEE Transactions on Software Engineering; Aug2002, Vol. 28 Issue 8, p735-746, 12p

Subject Terms: SOFTWARE protection, COMPUTER software, INTELLECTUAL property, DIGITAL watermarking, PIRACY (Copyright), DATA encryption

Engineering Models and Software Quality Models: An Example and a Discussion.

Authors: Salvaneschi, Paolo Piazzalunga, Ugo

Source: ICSE: International Conference on Software Engineering; 2008, p39-44, 6p, 2 Diagrams, 3 Charts

Subject Terms: COMPUTER software quality control, SOFTWARE engineering, TECHNICAL specifications, HYPOTHESIS, REASONING

Mining Privacy Goals from Privacy Policies Using Hybridized Task Recomposition.

Authors: BHATIA, JASPREET BREAUX, TRAVIS D. SCHAUB, FLORIAN

Source: ACM Transactions on Software Engineering & Methodology; May2016, Vol. 25 Issue 3, p1-2, 24p

Subject Terms: PRIVACY, SOFTWARE engineering, CROWDSOURCING, NATURAL language processing, DATA protection research

Artificial Immune System for Flight Envelope Estimation and Protection

Authors: Perhinschi, Mario G Moncayo, Hever Al Azzawi, Dia et al.

Contributors: Perhinschi, Mario G Moncayo, Hever Al Azzawi, Dia et al.

Source: DTIC

Subject Terms: Aircraft, Computer Programming and Software, Human Factors Engineering & Man Machine System, ADAPTIVE SYSTEMS, BIOTECHNOLOGY, FAULT DETECTION, FLIGHT ENVELOPE, FLIGHT SIMULATORS, AERODYNAMICS, COMPUTER AIDED MAINTENANCE, FALSE ALARMS, FIGHTER AIRCRAFT, IMMUNITY, KALMAN FILTERING, MAN COMPUTER INTERFACE, MATHEMATICAL MODELS, PERFORMANCE(ENGINEERING), SITUATIONAL AWARENESS, VECTOR ANALYSIS, ARTIFICIAL IMMUNE SYSTEMS, ACM(AIRCRAFT CONDITION MANAGEMENT), IMMUNOLOGICAL COMPUTATIONS

File Description: text/html

Relation: http://www.dtic.mil/docs/citations/ADA616775

Availability: http://www.dtic.mil/docs/citations/ADA616775
http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA616775

A Study on the Prevalence of Privacy in Software Engineering.

Authors: Sangaroonsilp, Pattaraporn Dam, Hoa Khanh

Source: ACM Computing Surveys; Nov2025, Vol. 57 Issue 11, p1-34, 34p

Subject Terms: HEALTH Insurance Portability & Accountability Act, SOFTWARE requirements specifications, DATA protection software, DATA protection, BEHAVIORAL targeting (Internet advertising), DATA privacy, INTERNET privacy

Structuring Communication Software for Quality-of-Service Guarantees.

Authors: Mehra, Ashish Indiresan, Atri Shin, Kang G.

Source: IEEE Transactions on Software Engineering; Oct97, Vol. 23 Issue 10, p616-634, 19p

Subject Terms: SOFTWARE engineering, INFORMATION resources management, COMPUTER software, COMPUTER networks, SOFTWARE protection, COMPUTER network protocols

On Protection by Layout Randomization.

Authors: Abadi, Martin Plotkin, Gordon D.

Source: ACM Transactions on Information & System Security (TISSEC); 2012, Vol. 15 Issue 2, p8-8:29, 29p

Subject Terms: RANDOMIZATION (Statistics), SOFTWARE protection, PROGRAMMING languages, COMPUTER software, COMPUTER science, COMPUTER access control

Using Personality Detection Tools for Software Engineering Research: How Far CanWe Go?

Authors: CALEFATO, FABIO LANUBILE, FILIPPO

Source: ACM Transactions on Software Engineering & Methodology; Jul2022, Vol. 31 Issue 3, p1-48, 48p

Subject Terms: SOFTWARE engineers, SOFTWARE development tools, SOFTWARE engineering, PERSONALITY

1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras

Authors: Okunola, Abiodun

Software engineering techniques for statically analyzing mobile apps: research trends, characteristics, and potential for industrial adoption.

Authors: Autili, Marco Malavolta, Ivano Perucci, Alexander et al.

Source: Journal of Internet Services & Applications; 7/23/2021, Vol. 12 Issue 1, p1-60, 60p

Subject Terms: MOBILE apps, SOFTWARE engineering, INDUSTRIAL capacity, SOFTWARE engineers, MOBILE operating systems

Viewpoint Personal Data and the Internet of Things: It is time to care about digital provenance.

Authors: Pasquier, Thomas Eyers, David Bacon, Jean

Source: Communications of the ACM; Jun2019, Vol. 62 Issue 6, p32-34, 3p

Subject Terms: INTERNET of things, DATA protection, INTERNET security, PERSONAL information management, SOFTWARE engineering

A Systematic Survey of Self-Protecting Software Systems.

Authors: YUAN, ERIC ESFAHANI, NAEEM MALEK, SAM

Source: ACM Transactions on Autonomous & Adaptive Systems; 2013, Vol. 8 Issue 4, p17:1-17:41, 41p

Subject Terms: SOFTWARE engineering, AUTONOMIC computing, COMPUTER security, ADAPTIVE computing systems, COMPUTER software, FUTURES studies

Integrating privacy debt and VSE's software developments.

Authors: Santamaria, Izaskun Larrucea, Xabier Fernandez‐Gauna, Borja

Source: Journal of Software: Evolution & Process; Aug2023, Vol. 35 Issue 8, p1-9, 9p

Subject Terms: COMPUTER software development, GENERAL Data Protection Regulation, 2016, NET present value, PRIVACY, DEBT