Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

SoK: Demystifying Privacy Enhancing Technologies Through the Lens of Software Developers (2401.00879v1)

Published 30 Dec 2023 in cs.SE and cs.CY

Abstract: In the absence of data protection measures, software applications lead to privacy breaches, posing threats to end-users and software organisations. Privacy Enhancing Technologies (PETs) are technical measures that protect personal data, thus minimising such privacy breaches. However, for software applications to deliver data protection using PETs, software developers should actively and correctly incorporate PETs into the software they develop. Therefore, to uncover ways to encourage and support developers to embed PETs into software, this Systematic Literature Review (SLR) analyses 39 empirical studies on developers' privacy practices. It reports the usage of six PETs in software application scenarios. Then, it discusses challenges developers face when integrating PETs into software, ranging from intrinsic challenges, such as the unawareness of PETs, to extrinsic challenges, such as the increased development cost. Next, the SLR presents the existing solutions to address these challenges, along with the limitations of the solutions. Further, it outlines future research avenues to better understand PETs from a developer perspective and minimise the challenges developers face when incorporating PETs into software.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (123)
  1. A survey on homomorphic encryption schemes: Theory and implementation. https://doi.org/10.1145/3214303
  2. Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 68, 13 pages. https://doi.org/10.1145/3411764.3445677
  3. Icek Ajzen. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 2 (1991), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  4. Abdulrahman Alhazmi and Nalin Asanka Gamagedara Arachchilage. 2021. I’m All Ears! Listening to Software Developers on Putting GDPR Principles into Software Development Practice. Personal Ubiquitous Comput. 25, 5 (may 2021), 879–892. https://doi.org/10.1007/s00779-021-01544-1
  5. Privacy by Design in Aged Care Monitoring Devices? Well, Not Quite Yet!. In Proceedings of the 32nd Australian Conference on Human-Computer Interaction (OzCHI ’20). Association for Computing Machinery, New York, NY, USA, 492–505. https://doi.org/10.1145/3441000.3441049
  6. Noura Alomar and Serge Egelman. 2022. Developers say the darnedest things: Privacy compliance processes followed by developers of child-directed apps. Proc. Priv. Enhancing Technol. 2022, 4 (Oct. 2022), 250–273.
  7. Privacy by Design and Software Engineering: A Systematic Literature Review. In Proceedings of the XXI Brazilian Symposium on Software Quality (Curitiba, Brazil) (SBQS ’22). Association for Computing Machinery, New York, NY, USA, Article 18, 10 pages. https://doi.org/10.1145/3571473.3571480
  8. Federated Learning for Healthcare: Systematic Review and Architecture Proposal. ACM Transactions on Intelligent Systems and Technology 13, 4 (Aug 2022), 1–23. https://doi.org/10.1145/3501813
  9. Apple and Google. 2021. Exposure Notification Privacy-preserving Analytics (ENPA). https://covid19-static.cdn-apple.com/applications/covid19/current/static/contact-tracing/pdf/ENPA_White_Paper.pdf
  10. Transitioning from testbeds to ships: an experience study in deploying the TIPPERS Internet of Things platform to the US Navy. Journal of Defense Modeling & Simulation 19, 3 (Jul 2022), 501–517. https://doi.org/10.1177/1548512920956383
  11. Understanding developers’ privacy and security mindsets via climate theory. Empir Software Eng 26, 6 (Nov 2021), 123. https://doi.org/10.1007/s10664-021-09995-z
  12. Generating Synthetic Data in Finance: Opportunities, Challenges and Pitfalls. In Proceedings of the First ACM International Conference on AI in Finance (New York, New York) (ICAIF ’20). Association for Computing Machinery, New York, NY, USA, Article 44, 8 pages. https://doi.org/10.1145/3383455.3422554
  13. Privacy Knowledge Base for Supporting Decision-Making in Software Development. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 13198 LNCS (2022), 147–157. https://doi.org/10.1007/978-3-030-98388-8_14
  14. Albert Bandura. 1977. Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review 84, 2 (1977), 191–215. https://doi.org/10.1037/0033-295X.84.2.191
  15. Data-Driven Development in Public Sector: How Agile Product Teams Maneuver Data Privacy Regulations. In International Conference on Agile Software Development. Springer, 165–180.
  16. ExDRa: Exploratory Data Science on Federated Raw Data. In Proceedings of the 2021 International Conference on Management of Data (Virtual Event, China) (SIGMOD ’21). Association for Computing Machinery, New York, NY, USA, 2450–2463. https://doi.org/10.1145/3448016.3457549
  17. Federated Data Preparation, Learning, and Debugging in Apache SystemDS. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (CIKM ’22). Association for Computing Machinery, New York, NY, USA, 4813–4817. https://doi.org/10.1145/3511808.3557162
  18. Overcoming Privacy-Related Challenges for Game Developers. In International Conference on Human-Computer Interaction. Springer, 18–28.
  19. Prochlo: Strong Privacy for Analytics in the Crowd. In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP ’17). Association for Computing Machinery, New York, NY, USA, 441–459. https://doi.org/10.1145/3132747.3132769 event-place: Shanghai, China.
  20. Taxonomy of educational objectives. the classification of educational goals: Affective domain. Longman.
  21. “I Never Thought About Securing My Machine Learning Systems”: A Study of Security and Privacy Awareness of Machine Learning Practitioners (MuC ’21). Association for Computing Machinery, New York, NY, USA, 520–546. https://doi.org/10.1145/3473856.3473869
  22. Virginia Braun and Victoria Clarke. 2021. Thematic analysis. SAGE Publications, London, England.
  23. MobHide: App-Level Runtime Data Anonymization on Mobile. Vol. 12418. Springer International Publishing, Cham, 490–507. https://doi.org/10.1007/978-3-030-61638-0_27
  24. “As We Grow, It Will Become a Priority”: American Mobile Start-Ups’ Privacy Practices. American Behavioral Scientist 62, 10 (Sep 2018), 1338–1355. https://doi.org/10.1177/0002764218787867
  25. Protecting the privacy of technology users who have cognitive disabilities: Identifying areas for improvement and targets for change. 7 (Jan 2020), 205566832095019. https://doi.org/10.1177/2055668320950195
  26. A survey on federated learning for security and privacy in healthcare applications. , 113–127 pages. https://doi.org/10.1016/j.comcom.2023.05.012
  27. Ben Collier and James Stewart. 2022. Privacy Worlds: Exploring Values and Design in the Development of the Tor Anonymity Network. Science, Technology, & Human Values 47, 5 (Sep 2022), 910–936. https://doi.org/10.1177/01622439211039019
  28. European Commission. 2023. Data protection in the EU. https://commission.europa.eu/law/law-topic/data-protection/data-protection-eu_en
  29. A survey of privacy enhancing technologies for smart cities. Pervasive and Mobile Computing 55 (Apr 2019), 76–95. https://doi.org/10.1016/j.pmcj.2019.03.001
  30. Fida K. Dankar and Khaled El Emam. 2013. Practicing differential privacy in health care: A review. , 35–67 pages.
  31. Fred Davis. 1985. A Technology Acceptance Model for Empirically Testing New End-User Information Systems. (01 1985).
  32. Perceptions of ICT Practitioners Regarding Software Privacy. Entropy 22, 4 (Apr 2020), 429. https://doi.org/10.3390/e22040429
  33. Differential Privacy in Practice: Expose your Epsilons! 9 (Oct 2019). https://doi.org/10.29012/jpc.689
  34. “Money Makes the World Go around”: Identifying Barriers to Better Privacy in Children’s Apps From Developers’ Perspectives. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 46, 15 pages. https://doi.org/10.1145/3411764.3445599
  35. European Commission. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance). https://eur-lex.europa.eu/eli/reg/2016/679/oj
  36. Liyue Fan and Ishan Gote. 2021. A Closer Look: Evaluating Location Privacy Empirically. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems (Beijing, China) (SIGSPATIAL ’21). Association for Computing Machinery, New York, NY, USA, 488–499. https://doi.org/10.1145/3474717.3484219
  37. PSI (ΨΨ\Psiroman_Ψ): a Private data Sharing Interface. ArXiv abs/1609.04340 (2016). https://api.semanticscholar.org/CorpusID:490798
  38. Nalin Asanka Gamagedara Arachchilage and Mumtaz Abdul Hameed. 2020. Designing a Serious Game: Teaching Developers to Embed Privacy into Software Systems. In 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). 7–12. https://doi.org/10.1145/3417113.3422149
  39. Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review. Journal of Network and Computer Applications 207 (Nov 2022), 103465. https://doi.org/10.1016/j.jnca.2022.103465
  40. Onion Routing. Commun. ACM 42, 2 (feb 1999), 39–41. https://doi.org/10.1145/293411.293443
  41. Privacy-Preserving Mobile Video Sharing using Fully Homomorphic Encryption. In 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). 1–3. https://doi.org/10.1109/PerComWorkshops48775.2020.9156217
  42. Daniel Greene and Katie Shilton. 2018. Platform privacies: Governance, collaboration, and the different meanings of “privacy” in iOS and Android development. New Media & Society 20, 4 (Apr 2018), 1640–1657. https://doi.org/10.1177/1461444817702397
  43. Privacy by Designers: Software Developers’ Privacy Mindset. In Proceedings of the 40th International Conference on Software Engineering (Gothenburg, Sweden) (ICSE ’18). Association for Computing Machinery, New York, NY, USA, 396. https://doi.org/10.1145/3180155.3182531
  44. Going Beyond Obscurity: Organizational Approaches to Data Anonymization. 2 (Nov 2018). https://doi.org/10.1145/3274335
  45. PrivC—A Framework for Efficient Secure Two-Party Computation. Vol. 305. Springer International Publishing, Cham, 394–407. https://doi.org/10.1007/978-3-030-37231-6_23
  46. Application of Federated Machine Learning in Manufacturing. In 2022 International Conference on Industry 4.0 Technology (I4Tech). IEEE, Pune, India, 1–8. https://doi.org/10.1109/I4Tech55392.2022.9952385
  47. Synthetic data generation for tabular health records: A systematic review. Neurocomputing 493 (Jul 2022), 28–45. https://doi.org/10.1016/j.neucom.2022.04.053
  48. “Those things are written by lawyers, and programmers are reading that.” Mapping the Communication Gap Between Software Developers and Privacy Experts. Proceedings on Privacy Enhancing Technologies 2024, 1 (Jan. 2024), 151–170. https://doi.org/10.56553/popets-2024-0010
  49. “Those things are written by lawyers, and programmers are reading that.” Mapping the Communication Gap Between Software Developers and Privacy Experts. Proceedings on Privacy Enhancing Technologies 1 (2024), 151–170.
  50. Haw-Bin How and Swee-Huay Heng. 2022. Blockchain-Enabled Searchable Encryption in Clouds: A Review. Journal of Information Security and Applications 67 (Jun 2022), 103183. https://doi.org/10.1016/j.jisa.2022.103183
  51. Ryoan: A Distributed Sandbox for Untrusted Computation on Secret Data. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association, Savannah, GA, 533–549. https://www.usenix.org/conference/osdi16/technical-sessions/presentation/hunt
  52. Javier Camacho Ibáñez and Mónica Villas Olmeda. 2022. Operationalising AI ethics: how are companies bridging the gap between practice and principles? An exploratory study. AI & Soc 37, 4 (Dec 2022), 1663–1687. https://doi.org/10.1007/s00146-021-01267-0
  53. Privacy Engineering in the Wild: Understanding the Practitioners’ Mindset, Organizational Aspects, and Current Practices. IEEE Transactions on Software Engineering 49, 9 (2023), 4324–4348. https://doi.org/10.1109/TSE.2023.3290237
  54. A comprehensive survey of digital twins and federated learning for industrial internet of things (IIoT), internet of vehicles (IoV) and internet of drones (IoD). Applied System Innovation 5, 3 (2022), 56.
  55. A review of preserving privacy in data collected from buildings with differential privacy. https://doi.org/10.1016/j.jobe.2022.104724
  56. Agile Islands in a Waterfall Environment: Challenges and Strategies in Automotive. In Proceedings of the 24th International Conference on Evaluation and Assessment in Software Engineering (Trondheim, Norway) (EASE ’20). Association for Computing Machinery, New York, NY, USA, 31–40. https://doi.org/10.1145/3383219.3383223
  57. Dilara Keküllüoğlu and Yasemin Acar. 2023. “ We are a startup to the core”: A qualitative interview study on the security and privacy development practices in Turkish software startups. In 2023 IEEE Symposium on Security and Privacy (SP). IEEE, 2015–2031.
  58. SAP HANA Goes Private: From Privacy Research to Privacy Aware Enterprise Analytics. Proc. VLDB Endow. 12, 12 (Aug 2019), 1998–2009. https://doi.org/10.14778/3352063.3352119
  59. A review on federated learning towards image processing. Computers and Electrical Engineering 99 (Apr 2022), 107818. https://doi.org/10.1016/j.compeleceng.2022.107818
  60. A Survey Of differential privacy-based techniques and their applicability to location-Based services. Computers & Security 111 (Dec 2021), 102464. https://doi.org/10.1016/j.cose.2021.102464
  61. Barbara Kitchenham and Stuart Charters. 2007. Guidelines for performing Systematic Literature Reviews in Software Engineering. 2 (01 2007).
  62. Understanding the Implementation of Technical Measures in the Process of Data Privacy Compliance: A Qualitative Study (ESEM ’22). Association for Computing Machinery, New York, NY, USA, 261–271. https://doi.org/10.1145/3544902.3546234
  63. Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation. J Transl Med 20, 1 (Oct 2022), 458. https://doi.org/10.1186/s12967-022-03671-6
  64. J Richard Landis and Gary G. Koch. 1977. The measurement of observer agreement for categorical data. Biometrics 33 1 (1977), 159–74.
  65. Coconut: An IDE Plugin for Developing Privacy-Friendly Apps. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 4, Article 178 (dec 2018), 35 pages. https://doi.org/10.1145/3287056
  66. How Developers Talk About Personal Data and What It Means for User Privacy: A Case Study of a Developer Forum on Reddit. Proc. ACM Hum.-Comput. Interact. 4, CSCW3, Article 220 (jan 2021), 28 pages. https://doi.org/10.1145/3432919
  67. Privacy-Preserving Contact Tracing Protocol for Mobile Devices: A Zero-Knowledge Proof Approach. Vol. 13107. Springer International Publishing, Cham, 327–344. https://doi.org/10.1007/978-3-030-93206-0_20
  68. FATE: An Industrial Grade Platform for Collaborative Learning with Data Protection. J. Mach. Learn. Res. 22, 1, Article 226 (jan 2021), 6 pages.
  69. A Systematic Literature Review on Federated Machine Learning: From a Software Engineering Perspective. Comput. Surveys 54, 5 (Jun 2022), 1–39. https://doi.org/10.1145/3450288
  70. Steve Mansfield-Devine. 2015. The Ashley Madison affair. Network Security 2015, 9 (2015), 8–16. https://doi.org/10.1016/S1353-4858(15)30080-5
  71. A survey on fully homomorphic encryption: An engineering perspective. https://doi.org/10.1145/3124441
  72. Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry. 2023 (Apr 2023), 151–170. https://doi.org/10.56553/popets-2023-0045
  73. Blockchain Meets Federated Learning in Healthcare: A Systematic Review With Challenges and Opportunities. IEEE Internet of Things Journal 10, 16 (2023), 14418–14437. https://doi.org/10.1109/JIOT.2023.3263598
  74. Visualizing Privacy-Utility Trade-Offs in Differentially Private Data Releases. Proceedings on Privacy Enhancing Technologies 2022 (2022), 601 – 618. https://api.semanticscholar.org/CorpusID:246016207
  75. Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling. Sensors 23, 14 (July 2023), 6443. https://doi.org/10.3390/s23146443
  76. A comprehensive review of federated learning for COVID‐19 detection. International Journal of Intelligent Systems 37, 3 (Mar 2022), 2371–2392. https://doi.org/10.1002/int.22777
  77. Darrel N Caulley Oam. 2007. Book review: Conducting research literature reviews: From the internet to paper. Eval. J. Australas. 7, 1 (March 2007), 66–67.
  78. Marie Caroline Oetzel and Sarah Spiekermann. 2014. A systematic methodology for privacy impact assessments: a design science approach. European Journal of Information Systems 23, 2 (Mar 2014), 126–150. https://doi.org/10.1057/ejis.2013.18
  79. Information Commissioner’s Office. 2023. Privacy-enhancing technologies (PETs). https://ico.org.uk/media/for-organisations/uk-gdpr-guidance-and-resources/data-sharing/privacy-enhancing-technologies-1-0.pdf
  80. The Perspective of Brazilian Software Developers on Data Privacy. J. Syst. Softw. 195, C (Jan 2023). https://doi.org/10.1016/j.jss.2022.111523
  81. Designing Privacy-aware Internet of Things Applications. Information Sciences 512 (09 2019). https://doi.org/10.1016/j.ins.2019.09.061
  82. Mark Petticrew and Helen Roberts. 2006. Systematic Reviews in the Social Sciences: A Practical Guide. Vol. 11. https://doi.org/10.1002/9780470754887
  83. A Survey of Deep Learning Architectures for Privacy-Preserving Machine Learning With Fully Homomorphic Encryption. , 117477–117500 pages. https://doi.org/10.1109/ACCESS.2022.3219049
  84. MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 16, 4 (Jul 2019), 1328–1341. https://doi.org/10.1109/TCBB.2018.2854776
  85. Rivka Ribak. 2019. Translating privacy: developer cultures in the global world of practice. Information, Communication & Society 22, 6 (2019), 838–853.
  86. Privacy Compliance in Software Development: A Guide to Implementing the LGPD Principles. In Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing (SAC ’23). Association for Computing Machinery, New York, NY, USA, 1352–1361. https://doi.org/10.1145/3555776.3577615 event-place: Tallinn, Estonia.
  87. Per Runeson and Martin Höst. 2009. Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering 14, 2 (April 2009), 131–164. https://doi.org/10.1007/s10664-008-9102-8
  88. AI ethics principles in practice: Perspectives of designers and developers. IEEE Transactions on Technology and Society (2023).
  89. Don’t Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3544548.3580791
  90. SEAL 2023. Microsoft SEAL (release 4.1). https://github.com/Microsoft/SEAL. Microsoft Research, Redmond, WA..
  91. Awanthika Senarath and Nalin Arachchilage. 2018a. Why developers cannot embed privacy into software systems?: An empirical investigation. 211–216. https://doi.org/10.1145/3210459.3210484
  92. Awanthika Senarath and Nalin Asanka Gamagedara Arachchilage. 2018b. Understanding software developers’ approach towards implementing data Minimization. The 4th Workshop on Security Information Workers (WSIW), 14th Symposium on Usability, Privacy, and Security, USENIX (Aug. 2018).
  93. Awanthika Senarath and Nalin Asanka Gamagedara Arachchilage. 2021. The Unheard Story of Organizational Motivations Towards User Privacy. In Research Anthology on Privatizing and Securing Data. IGI Global, 231–254.
  94. Will They Use It or Not? Investigating Software Developers’ Intention to Follow Privacy Engineering Methodologies. ACM Trans. Priv. Secur. 22, 4, Article 23 (nov 2019), 30 pages. https://doi.org/10.1145/3364224
  95. A systematic review of federated learning from clients’ perspective: challenges and solutions. Artificial Intelligence Review (2023), 1–55.
  96. Role-Playing Computer Ethics: Designing and Evaluating the Privacy by Design (PbD) Simulation. 26 (Dec. 2020), 2911–2926. https://doi.org/10.1007/s11948-020-00250-0
  97. A Detailed Survey on Federated Learning Attacks and Defenses. https://doi.org/10.3390/electronics12020260
  98. Md Fahimuzzman Sohan and Anas Basalamah. 2023. A Systematic Review on Federated Learning in Medical Image Analysis. , 28628–28644 pages. https://doi.org/10.1109/ACCESS.2023.3260027
  99. Inside the Organization: Why Privacy and Security Engineering Is a Challenge for Engineers. Proc. IEEE 107, 3 (2019), 600–615. https://doi.org/10.1109/JPROC.2018.2866769
  100. Organisational responses to the ethical issues of artificial intelligence. AI & SOCIETY 37 (03 2022), 1–15. https://doi.org/10.1007/s00146-021-01148-6
  101. A survey on secure computation based on homomorphic encryption in vehicular Ad Hoc networks. , 31 pages. https://doi.org/10.3390/s20154253
  102. B. Suruliraj and R. Orji. 2022. Federated Learning Framework for Mobile Sensing Apps in Mental Health. In SeGAH 2022 - 2022 IEEE 10th International Conference on Serious Games and Applications for Health. https://doi.org/10.1109/SEGAH54908.2022.9978600
  103. Latanya Sweeney. 2000. Simple demographics often identify people uniquely. Health (San Francisco) 671, 2000 (2000), 1–34.
  104. Latanya Sweeney. 2002. K-Anonymity: A Model for Protecting Privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10, 5 (oct 2002), 557–570. https://doi.org/10.1142/S0218488502001648
  105. Privacy Champions in Software Teams: Understanding Their Motivations, Strategies, and Challenges. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI ’21). Association for Computing Machinery, New York, NY, USA, Article 693, 15 pages. https://doi.org/10.1145/3411764.3445768
  106. Differential Privacy Team. 2017. differential-privacy. https://github.com/google/differential-privacy.
  107. Your apps know where you were Last night, and they’re not keeping it secret. https://www.nytimes.com/interactive/2018/12/10/business/location-data-privacy-apps.html
  108. Evaluation of Software Development Life Cycle: Methodology Implementation. SIGSOFT Softw. Eng. Notes 7, 1 (jan 1982), 45–60. https://doi.org/10.1145/1010809.1010816
  109. User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly 27, 3 (2003), 425. https://doi.org/10.2307/30036540
  110. Building and Maintaining a Third-Party Library Supply Chain for Productive and Secure SGX Enclave Development. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: Software Engineering in Practice (Seoul, South Korea) (ICSE-SEIP ’20). Association for Computing Machinery, New York, NY, USA, 100–109. https://doi.org/10.1145/3377813.3381348
  111. EMP-toolkit: Efficient MultiParty computation toolkit. https://github.com/emp-toolkit.
  112. Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review. IEEE Internet of Things Journal 10, 4 (2023), 3642–3663. https://doi.org/10.1109/JIOT.2022.3231363
  113. Claes Wohlin. 2014. Guidelines for Snowballing in Systematic Literature Studies and a Replication in Software Engineering. In Proceedings of the 18th International Conference on Evaluation and Assessment in Software Engineering (London, England, United Kingdom) (EASE ’14). Association for Computing Machinery, New York, NY, USA, Article 38, 10 pages. https://doi.org/10.1145/2601248.2601268
  114. A survey of noninteractive zero knowledge proof system and its applications. The Scientific World Journal 2014 (2014).
  115. A Survey of the Social Internet of Vehicles: Secure Data Issues, Solutions, and Federated Learning. IEEE Intelligent Transportation Systems Magazine 15, 2 (2022), 70–84.
  116. Federated Learning of Gboard Language Models with Differential Privacy. arXiv:2305.18465 (July 2023). http://arxiv.org/abs/2305.18465 arXiv:2305.18465 [cs].
  117. Symmetry in Privacy-Based Healthcare: A Review of Skin Cancer Detection and Classification Using Federated Learning. https://doi.org/10.3390/sym15071369
  118. Towards a Privacy Preserving Cohort Discovery Framework for Clinical Research Networks. J. of Biomedical Informatics 66, C (feb 2017), 42–51. https://doi.org/10.1016/j.jbi.2016.12.008
  119. Adrian Zbiciak and Tymon Markiewicz. 2023. A new extraordinary means of appeal in the Polish criminal procedure: the basic principles of a fair trial and a complaint against a cassatory judgment. Access to Justice in Eastern Europe 6, 2 (March 2023), 1–18.
  120. Towards Federated Learning: A Case Study in the Telecommunication Domain. Vol. 434. Springer International Publishing, Cham, 238–253. https://doi.org/10.1007/978-3-030-91983-2_18
  121. Challenges and future directions of secure federated learning: a survey. https://doi.org/10.1007/s11704-021-0598-z
  122. A Review of Homomorphic Encryption and its Applications. In Proceedings of the 9th EAI International Conference on Mobile Multimedia Communications. ACM, Xi’an, People’s Republic of China. https://doi.org/10.4108/eai.18-6-2016.2264201
  123. Felicitas: Federated Learning in Distributed Cross Device Collaborative Frameworks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22). Association for Computing Machinery, New York, NY, USA, 4502–4509. https://doi.org/10.1145/3534678.3539039
Citations (3)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now