research-article

Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing Technologies

Authors:
Victoria L. Lemieux

School of Information, University of British Columbia, Canada

School of Information, University of British Columbia, Canada

0000-0003-1339-6289
View Profile

,
John Werner

School of Information, University of British Columbia, Canada

School of Information, University of British Columbia, Canada

0000-0002-8175-5047
View Profile

Authors Info & Claims

Journal on Computing and Cultural Heritage Volume 16 Issue 4Article No.: 83pp 1–18https://doi.org/10.1145/3633477

Published:08 January 2024Publication History

Journal on Computing and Cultural Heritage

Abstract

With increased concerns about data protection and privacy over the past several years, and concomitant introduction of regulations restricting access to personal information (PI), archivists in many jurisdictions now must undertake ‘sensitivity reviews’ of archival documents to determine whether they can make those documents accessible to researchers. Such reviews are onerous given increasing volume of records and complex due to how difficult it can be for archivists to identify whether records contain PI under the provisions of various laws. Despite research into the application of tools and techniques to automate sensitivity reviews, effective solutions remain elusive. Not yet explored as a solution to the challenge of enabling access to archival holdings subject to privacy restrictions is the application of privacy-enhancing technologies (PETs) —a class of emerging technologies that rest on the assumption that a body of documents is confidential or private and must remain so. While seemingly being counterintuitive to apply PETs to making archives more accessible, we argue that PETs could provide an opportunity to protect PI in archival holdings whilst still enabling research on those holdings. In this article, to lay a foundation for archival experimentation with use of PETs, we contribute an overview of these technologies based on a scoping review and discuss possible use cases and future research directions.

REFERENCES

[1] Abril Daniel, Navarro-Arribas Guillermo, and Torra Vicenç. 2011. On the declassification of confidential documents. In Modeling Decision for Artificial Intelligence: 8th International Conference, MDAI 2011, Changsha, Hunan, China, July 28–30, 2011, Proceedings 8. Springer, 235–246.Google ScholarCross Ref
[2] Abufadda Mohammad and Mansour Khalid. 2021. A survey of synthetic data generation for machine learning. In 2021 22nd International Arab Conference on Information Technology (ACIT ’21). IEEE, 1–7.Google ScholarCross Ref
[3] Acar Abbas, Aksu Hidayet, Uluagac A. Selcuk, and Conti Mauro. 2018. A survey on homomorphic encryption schemes: Theory and implementation. ACM Computing Surveys (CSUR) 51, 4 (2018), 1–35.Google ScholarDigital Library
[4] Agency Defense Advanced Research Projects. 2010. New Technologies to Support Declassification. DARPA-SN-10-73. Retrieved March 5, 2023 from https://sgp.fas.org/news/2010/09/darpa-declass.pdfGoogle Scholar
[5] Amiri Wesam Al, Baza Mohamed, Banawan Karim, Mahmoud Mohamed, Alasmary Waleed, and Akkaya Kemal. 2019. Privacy-preserving smart parking system using blockchain and private information retrieval. In 2019 International Conference on Smart Applications, Communications and Networking (SmartNets). IEEE, 1–6.Google ScholarCross Ref
[6] Badawi Ahmad Al, Hoang Louie, Mun Chan Fook, Laine Kim, and Aung Khin Mi Mi. 2020. PrivFT: Private and fast text classification with homomorphic encryption. IEEE Access 8 (2020), 226544–226556.Google ScholarCross Ref
[7] Alawad Mohammed, Yoon Hong-Jun, Gao Shang, Mumphrey Brent, Wu Xiao-Cheng, Durbin Eric B., Jeong Jong Cheol, Hands Isaac, Rust David, Coyle Linda, et al. 2020. Privacy-preserving deep learning NLP models for cancer registries. IEEE Transactions on Emerging Topics in Computing 9, 3 (2020), 1219–1230.Google ScholarCross Ref
[8] Allan Alex. 2015. Review of Government Digital Records. Retrieved March 5, 2023 from https://www.gov.uk/government/publications/government-digital-records-and-archives-review-by-sir-alex-allanGoogle Scholar
[9] Aloufi Asma, Hu Peizhao, Song Yongsoo, and Lauter Kristin. 2021. Computing blindfolded on data homomorphically encrypted under multiple keys: A survey. ACM Computing Surveys (CSUR) 54, 9 (2021), 1–37.Google ScholarDigital Library
[10] Archer David W., Bogdanov Dan, Lindell Yehuda, Kamm Liina, Nielsen Kurt, Pagter Jakob Illeborg, Smart Nigel P., and Wright Rebecca N.. 2018. From keys to databases-real-world applications of secure multi-party computation. Comput. J. 61, 12 (2018), 1749–1771.Google Scholar
[11] Archives National. 2016. The Application of Technology-assisted Review to Born-digital Records Transfer, Inquiries and Beyond. Retrieved February 26, 2023 from http://www.nationalarchives.gov.uk/documents/technology-assisted-review-to-born-digital-records-transfer.pdfGoogle Scholar
[12] Baron Jason R., Sayed Mahmoud F., and Oard Douglas W.. 2022. Providing more efficient access to government records: A use case involving application of machine learning to improve FOIA Review for the deliberative process privilege. ACM Journal on Computing and Cultural Heritage (JOCCH) 15, 1 (2022), 1–19.Google ScholarDigital Library
[13] Bell Mark, Storrar Tom, and Winters Jane. 2022. Web archives and the problem of access: Prototyping a researcher dashboard for the UK government web archive. In Archives, Access and Artificial Intelligence. Bielefeld University Press, 61–82.Google Scholar
[14] Blanco-Justicia Alberto, Sánchez David, Domingo-Ferrer Josep, and Muralidhar Krishnamurty. 2022. A critical review on the use (and misuse) of differential privacy in machine learning. Comput. Surveys 55, 8 (2022), 1–16.Google ScholarDigital Library
[15] Boulemtafes Amine, Derhab Abdelouahid, Braham Nassim Ait Ali, and Challal Yacine. 2021. PReDIHERO–privacy-preserving remote deep learning inference based on homomorphic encryption and reversible obfuscation for enhanced client-side overhead in pervasive health monitoring. In 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA ’21). IEEE, 1–8.Google Scholar
[16] Cabrero-Holgueras José and Pastrana Sergio. 2021. SoK: Privacy-preserving computation techniques for deep learning. Proceedings on Privacy Enhancing Technologies 2021, 4 (2021), 139–162.Google ScholarCross Ref
[17] Chatel Sylvain, Pyrgelis Apostolos, Troncoso-Pastoriza Juan Ramón, and Hubaux Jean-Pierre. 2021. SoK: Privacy-preserving collaborative tree-based model learning. Proceedings on Privacy Enhancing Technologies 2021, 3 (2021), 182–203.Google ScholarCross Ref
[18] Creeger Mache. 2022. The rise of fully homomorphic encryption: Often called the holy grail of cryptography, commercial FHE is near. Queue 20, 4 (2022), 39–60.Google ScholarDigital Library
[19] Desfontaines Damien and Pejó Balázs. 2020. SoK: Differential privacies. Proceedings on Privacy Enhancing Technologies 2020, 2 (2020), 288–313.Google ScholarCross Ref
[20] Devlin Jacob, Chang Ming-Wei, Lee Kenton, and Toutanova Kristina. 2018. BERT: Pre-training of deep bidirectional Transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).Google Scholar
[21] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of Cryptography: Third Theory of Cryptography Conference (TCC’06), New York, NY, USA, March 4-7, 2006. 265–284.Google Scholar
[22] Evans Joanne, McKemmish Sue, and Rolan Gregory. 2019. Participatory information governance: Transforming recordkeeping for childhood out-of-home care. Records Management Journal 29, 1/2 (2019), 178–193.Google ScholarCross Ref
[23] Fei Shufan, Yan Zheng, Ding Wenxiu, and Xie Haomeng. 2021. Security vulnerabilities of SGX and countermeasures: A survey. ACM Computing Surveys (CSUR) 54, 6 (2021), 1–36.Google ScholarDigital Library
[24] Felsen Susanne, Kiss Ágnes, Schneider Thomas, and Weinert Christian. 2019. Secure and private function evaluation with Intel SGX. In Proceedings of the 2019 ACM SIGSAC Conference on Cloud Computing Security Workshop. 165–181.Google ScholarDigital Library
[25] Fioretto Ferdinando, Lee Chansoo, and Hentenryck Pascal Van. 2018. Constrained-based differential privacy for mobility services. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 1405–1413.Google ScholarDigital Library
[26] Fiorucci Marco, Khoroshiltseva Marina, Pontil Massimiliano, Traviglia Arianna, Bue Alessio Del, and James Stuart. 2020. Machine learning for cultural heritage: A survey. Pattern Recognition Letters 133 (2020), 102–108.Google ScholarCross Ref
[27] Fletcher Sam and Islam Md Zahidul. 2019. Decision tree classification with differential privacy: A survey. ACM Computing Surveys (CSUR) 52, 4 (2019), 1–33.Google ScholarDigital Library
[28] Franks Jason. 2022. Text classification for records management. Journal on Computing and Cultural Heritage (JOCCH) 15, 3 (2022), 1–19.Google ScholarDigital Library
[29] Gentry Craig. 2009. Fully homomorphic encryption using ideal lattices. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing. 169–178.Google ScholarDigital Library
[30] Gibson J. Paul, Krimmer Robert, Teague Vanessa, and Pomares Julia. 2016. A review of e-voting: The past, present and future. Annals of Telecommunications 71 (2016), 279–286.Google ScholarCross Ref
[31] Gilliland Anne J.. 2017. A matter of life or death: A critical examination of the role of records and archives in supporting the agency of the forcibly displaced. Journal of Critical Library and Information Studies 1, 2 (2017).Google ScholarCross Ref
[32] Golding Frank, Lewis Antonina, McKemmish Sue, Rolan Gregory, and Thorpe Kirsten. 2021. Rights in records: A charter of lifelong rights in childhood recordkeeping in out-of-home care for Australian and Indigenous Australian children and care leavers. The International Journal of Human Rights 25, 9 (2021), 1625–1657.Google ScholarCross Ref
[33] Gollins Timothy, McDonald Graham, Macdonald Craig, and Ounis Iadh. 2014. On using information retrieval for the selection and sensitivity review of digital public records. In PIR@ SIGIR. 39–40.Google Scholar
[34] Gupta Dilip, Saul Melissa, and Gilbertson John. 2004. Evaluation of a deidentification (De-Id) software engine to share pathology reports and clinical documents for research. American Journal of Clinical Pathology 121, 2 (2004), 176–186.Google ScholarCross Ref
[35] Gupta Shreya and Arora Ginni. 2019. Use of homomorphic encryption with GPS in location privacy. In 2019 4th International Conference on Information Systems and Computer Networks (ISCON ’19). IEEE, 42–45.Google ScholarCross Ref
[36] Hamza Rafik and Zettsu Koji. 2021. Investigation on privacy-preserving techniques for personal data. In Proceedings of the 2021 Workshop on Intelligent Cross-Data Analysis and Retrieval. 62–66.Google ScholarDigital Library
[37] Haralampieva Veneta, Rueckert Daniel, and Passerat-Palmbach Jonathan. 2020. A systematic comparison of encrypted machine learning solutions for image classification. In Proceedings of the 2020 Workshop on Privacy-preserving Machine Learning in Practice. 55–59.Google ScholarDigital Library
[38] Hegde Aditya, Möllering Helen, Schneider Thomas, and Yalame Hossein. 2021. SoK: Efficient privacy-preserving clustering. Cryptology ePrint Archive (2021).Google Scholar
[39] Hitaj Briland, Ateniese Giuseppe, and Perez-Cruz Fernando. 2017. Deep models under the GAN: Information leakage from collaborative deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. 603–618.Google ScholarDigital Library
[40] House The White. 2021. US and UK to Partner on Prize Challenges to Advance Privacy-Enhancing Technologies. Retrieved March 5, 2023 from https://www.whitehouse.gov/ostp/news-updates/2021/12/08/us-and-uk-to-partner-on-a-prize-challenges-to-advance-privacy-enhancing-technologies/Google Scholar
[41] Hutchinson Tim. 2017. Protecting privacy in the archives: Preliminary explorations of topic modeling for born-digital collections. In 2017 IEEE International Conference on Big Data (Big Data ’17). IEEE, 2251–2255.Google ScholarCross Ref
[42] InterPARES. 2023. Homepage. Retrieved March 4, 2023 from https://interparestrustai.org/Google Scholar
[43] Issa Wael, Moustafa Nour, Turnbull Benjamin, Sohrabi Nasrin, and Tari Zahir. 2023. Blockchain-based federated learning for securing Internet of Things: A comprehensive survey. Comput. Surveys 55, 9 (2023), 1–43.Google ScholarDigital Library
[44] j9recurses. 2015. ArchExtract. Retrieved March 5, 2023 from https://github.com/j9recurses/archextractGoogle Scholar
[45] Jaillant Lise. 2022. Archives, Access and Artificial Intelligence: Working with Born-digital and Digitized Archival Collections. Bielefeld University Press.Google Scholar
[46] Jiang Xue, Zhou Xuebing, and Grossklags Jens. 2022. Privacy-preserving high-dimensional data collection with federated generative autoencoder. Proc. Priv. Enhancing Technol. 2022, 1 (2022), 481–500.Google ScholarCross Ref
[47] Jordon James, Szpruch Lukasz, Houssiau Florimond, Bottarelli Mirko, Cherubin Giovanni, Maple Carsten, Cohen Samuel N., and Weller Adrian. 2022. Synthetic data–what, why and how? arXiv preprint arXiv:2205.03257 (2022).Google Scholar
[48] Kang Meng and Lemieux Victoria. 2021. A decentralized identity-based blockchain solution for privacy-preserving licensing of individual-controlled data to prevent unauthorized secondary data usage. Ledger 6 (2021).Google ScholarCross Ref
[49] Kitchenham Barbara, Brereton O. Pearl, Budgen David, Turner Mark, Bailey John, and Linkman Stephen. 2009. Systematic literature reviews in software engineering–a systematic literature review. Information and Software Technology 51, 1 (2009), 7–15.Google ScholarDigital Library
[50] LeClere Ellen. 2018. Breaking rules for good? How archivists manage privacy in large-scale digitisation projects. Archives and Manuscripts 46, 3 (2018), 289–308.Google ScholarCross Ref
[51] Lemieux Victoria, Voskobojnikov Artemij, and Kang Meng. 2021. Addressing audit and accountability issues in self-sovereign identity blockchain systems using archival science principles. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC ’21). IEEE, 1210–1216.Google ScholarCross Ref
[52] Li Yijing, Tao Xiaofeng, Zhang Xuefei, Wang Mingsi, and Wang Shuo. 2022. Break the data barriers while keeping privacy: A graph differential privacy method. IEEE Internet of Things Journal (2022).Google Scholar
[53] Lianto Hans Albert, Zhao Yang, and Zhao Jun. 2020. POSTER: Attacks to federated learning: Responsive web user interface to recover training data from user gradients. In Proceedings of the 15th ACM Asia Conference on Computer and Communications Security. 901–903.Google ScholarDigital Library
[54] Lindell Yehuda. 2020. Secure multiparty computation. Commun. ACM 64, 1 (2020), 86–96.Google ScholarDigital Library
[55] Liu Bo, Ding Ming, Shaham Sina, Rahayu Wenny, Farokhi Farhad, and Lin Zihuai. 2021. When machine learning meets privacy: A survey and outlook. ACM Computing Surveys (CSUR) 54, 2 (2021), 1–36.Google ScholarDigital Library
[56] Loukil Faiza, Ghedira-Guegan Chirine, Boukadi Khouloud, Benharkat Aïcha-Nabila, and Benkhelifa Elhadj. 2021. Data privacy based on IoT device behavior control using blockchain. ACM Transactions on Internet Technology (TOIT) 21, 1 (2021), 1–20.Google ScholarDigital Library
[57] Lu Chang, Batista Danielle, Hamouda Hoda, Lemieux Victoria, et al. 2020. Consumers’ intentions to adopt blockchain-based personal health records and data sharing: Focus group study. JMIR Formative Research 4, 11 (2020), e21995.Google ScholarCross Ref
[58] Marciano Richard, Lemieux Victoria, Hedges Mark, Esteva Maria, Underwood William, Kurtz Michael, and Conrad Mark. 2018. Archival records and training in the age of big data. In Re-Envisioning the MLS: Perspectives on the future of library and information science education. Emerald Publishing Limited.Google Scholar
[59] McDonald Graham. 2015. A framework for enhanced text classification in sensitivity and reputation management. In 6th BCS-IRSG Symposium on Future Directions in Information Access (FDIA ’15) 6. 56–58.Google Scholar
[60] McDonald Graham. 2021. A framework for technology-assisted sensitivity review: Using sensitivity classification to prioritise documents for review. In ACM SIGIR Forum, Vol. 53. ACM New York, NY, USA, 42–43.Google Scholar
[61] Mcdonald Graham, Macdonald Craig, and Ounis Iadh. 2020. How the accuracy and confidence of sensitivity classification affects digital sensitivity review. ACM Transactions on Information Systems (TOIS) 39, 1 (2020), 1–34.Google ScholarDigital Library
[62] Mo Ran, Liu Jianfeng, Yu Wentao, Jiang Fu, Gu Xin, Zhao Xiaoshuai, Liu Weirong, and Peng Jun. 2019. A differential privacy-based protecting data preprocessing method for big data mining. In 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE ’19). IEEE, 693–699.Google ScholarCross Ref
[63] Morita Kazunari, Yoshimura Hiroki, Nishiyama Masashi, and Iwai Yoshio. 2018. Protecting personal information using homomorphic encryption for person re-identification. In 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE ’18). IEEE, 166–167.Google ScholarCross Ref
[64] Nandakumar Karthik, Ratha Nalini, Pankanti Sharath, and Halevi Shai. 2019. Towards deep neural network training on encrypted data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 0–0.Google ScholarCross Ref
[65] Neamatullah Ishna, Douglass Margaret M., Lehman Li-Wei H., Reisner Andrew, Villarroel Mauricio, Long William J., Szolovits Peter, Moody George B., Mark Roger G., and Clifford Gari D.. 2008. Automated de-identification of free-text medical records. BMC Medical Informatics and Decision Making 8, 1 (2008), 1–17.Google ScholarCross Ref
[66] Nettleton David F. and Abril Daniel. 2012. Document sanitization: Measuring search engine information loss and risk of disclosure for the Wikileaks cables. In Privacy in Statistical Databases: UNESCO Chair in Data Privacy, International Conference, PSD 2012, Palermo, Italy, September 26–28, 2012. Proceedings. Springer, 308–321.Google ScholarDigital Library
[67] Statistics UK Office of National. 2021. Confidentiality. Retrieved March 5, 2023 from https://www.ons.gov.uk/census/2011census/confidentiality#::text=Census%20records%20are%20kept%20confidential,public%20release%20before%20January%202112Google Scholar
[68] Pascoal Túlio, Decouchant Jérémie, and Völp Marcus. 2022. Secure and distributed assessment of privacy-preserving GWAS releases. In Proceedings of the 23rd ACM/IFIP International Middleware Conference. 308–321.Google ScholarDigital Library
[69] Payne Nathaniel and Baron Jason R.. 2017. Auto-categorization methods for digital archives. In 2017 IEEE International Conference on Big Data (Big Data ’17). IEEE, 2288–2298.Google ScholarCross Ref
[70] Pisoni Galena, Díaz-Rodríguez Natalia, Gijlers Hannie, and Tonolli Linda. 2021. Human-centered artificial intelligence for designing accessible cultural heritage. Applied Sciences 11, 2 (2021), 870.Google ScholarCross Ref
[71] Qu Youyang, Uddin Md Palash, Gan Chenquan, Xiang Yong, Gao Longxiang, and Yearwood John. 2022. Blockchain-enabled federated learning: A survey. Comput. Surveys 55, 4 (2022), 1–35.Google ScholarDigital Library
[72] Raghunathan Trivellore E.. 2021. Synthetic data. Annual Review of Statistics and Its Application 8 (2021), 129–140.Google ScholarCross Ref
[73] Regev Oded. 2006. Lattice-based cryptography. In Advances in Cryptology-CRYPTO 2006: 26th Annual International Cryptology Conference, Santa Barbara, California, USA, August 20–24, 2006. Proceedings 26. Springer, 131–141.Google ScholarDigital Library
[74] Rolan Gregory, Phan Han Duy, and Evans Joanne. 2020. Recordkeeping and relationships: Designing for lifelong information rights. In Proceedings of the 2020 ACM Designing Interactive Systems Conference. 205–218.Google ScholarDigital Library
[75] Romps Nancy. 2023. Searching for solutions: MITRE tool simplifies freedom of information act requests. MITRE News & Insights (2023), online. Retrieved Retrieved March 5, 2023 from from https://www.mitre.org/news-insights/impact-story/mitre-tool-simplifies-freedom-information-act-requestsGoogle Scholar
[76] Rubin Donald B.. 1993. Statistical disclosure limitation. Journal of Official Statistics 9, 2 (1993), 461–468.Google Scholar
[77] Sánchez David, Batet Montserrat, and Viejo Alexandre. 2012. Detecting sensitive information from textual documents: An information-theoretic approach. In Modeling Decisions for Artificial Intelligence: 9th International Conference, MDAI 2012, Girona, Catalonia, Spain, November 21–23, 2012. Proceedings 9. Springer, 173–184.Google ScholarDigital Library
[78] Sedlmeir Johannes, Smethurst Reilly, Rieger Alexander, and Fridgen Gilbert. 2021. Digital identities and verifiable credentials. Business & Information Systems Engineering 63, 5 (2021), 603–613.Google ScholarCross Ref
[79] Shan Zihao, Ren Kui, Blanton Marina, and Wang Cong. 2018. Practical secure computation outsourcing: A survey. ACM Computing Surveys (CSUR) 51, 2 (2018), 1–40.Google ScholarDigital Library
[80] Sloyan Victoria. 2016. Born-digital archives at the Wellcome Library: Appraisal and sensitivity review of two hard drives. Archives and Records 37, 1 (2016), 20–36.Google ScholarCross Ref
[81] Society The Royal. 2019. Protecting Privacy in Practice: The Current Use, Development and Limits of Privacy Enhancing Technologies in Data Analysis. Retrieved March 5, 2023 from https://royalsociety.org/-/media/policy/projects/privacy-enhancing-technologies/Protecting-privacy-in-practice.pdfGoogle Scholar
[82] Sousa Samuel and Kern Roman. 2022. How to keep text private? A systematic review of deep learning methods for privacy-preserving natural language processing. Artificial Intelligence Review (2022), 1–66.Google Scholar
[83] Sweeney Latanya. 1996. Replacing personally-identifying information in medical records, the Scrub system. In Proceedings of the AMIA Annual Fall Symposium. American Medical Informatics Association, 333.Google Scholar
[84] Team DFINITY et al. 2022. The internet computer for geeks. Cryptology ePrint Archive (2022).Google Scholar
[85] Treiber Amos, Müllmann Dirk, Schneider Thomas, and Döhmann Indra Spiecker Genannt. 2022. Data protection law and multi-party computation: Applications to information exchange between law enforcement agencies. In Proceedings of the 21st Workshop on Privacy in the Electronic Society. 69–82.Google ScholarDigital Library
[86] Tsoutsos Nektarios Georgios and Maniatakos Michail. 2017. Efficient detection for malicious and random errors in additive encrypted computation. IEEE Trans. Comput. 67, 1 (2017), 16–31.Google ScholarDigital Library
[87] Usynin Dmitrii, Ziller Alexander, Makowski Marcus, Braren Rickmer, Rueckert Daniel, Glocker Ben, Kaissis Georgios, and Passerat-Palmbach Jonathan. 2021. Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nature Machine Intelligence 3, 9 (2021), 749–758.Google ScholarCross Ref
[88] Vepakomma Praneeth, Swedish Tristan, Raskar Ramesh, Gupta Otkrist, and Dubey Abhimanyu. 2018. No peek: A survey of private distributed deep learning. arXiv preprint arXiv:1812.03288 (2018).Google Scholar
[89] Vignesh R., Vishnu R., Raj Sreenu M., Akshay M. B., Nair Divya G., and Nair Jyothisha R.. 2019. An improved method for sharing medical images for privacy preserving machine learning using multiparty computation and steganography. In 2019 9th International Conference on Advances in Computing and Communication (ICACC ’19). IEEE, 42–45.Google ScholarCross Ref
[90] Wood Alexander, Najarian Kayvan, and Kahrobaei Delaram. 2020. Homomorphic encryption for machine learning in medicine and bioinformatics. ACM Computing Surveys (CSUR) 53, 4 (2020), 1–35.Google ScholarDigital Library
[91] Yadav Vijay Kumar, Andola Nitish, Verma Shekhar, and Venkatesan S.. 2022. A survey of oblivious transfer protocol. ACM Computing Surveys (CSUR) 54, 10s (2022), 1–37.Google ScholarDigital Library
[92] Yang Qiang, Liu Yang, Chen Tianjian, and Tong Yongxin. 2019. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10, 2 (2019), 1–19.Google ScholarDigital Library
[93] Zhang Jing-Wen, Xu Gang, Chen Xiu-Bo, Chang Yan, and Dong Zhi-Chao. 2023. Improved multiparty quantum private comparison based on quantum homomorphic encryption. Physica A: Statistical Mechanics and its Applications 610 (2023), 128397.Google ScholarCross Ref
[94] Zhao Ying and Chen Jinjun. 2022. A survey on differential privacy for unstructured data content. ACM Computing Surveys (CSUR) 54, 10s (2022), 1–28.Google ScholarDigital Library
[95] Zhou Tanping, Liu Wenchao, Li Ningbo, Yang Xiaoyuan, Han Yiliang, and Zheng Shangwen. 2021. Secure scheme for locating disease-causing genes based on multi-key homomorphic encryption. Tsinghua Science and Technology 27, 2 (2021), 333–343.Google ScholarCross Ref
[96] ZKProof.Org. 2022. ZKProof Community Reference. Retrieved March 5, 2023 from https://docs.zkproof.org/reference.pdfGoogle Scholar

Index Terms

Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing Technologies
1. Security and privacy
  1. Database and storage security
    1. Information accountability and usage control
    2. Management and querying of encrypted data
  2. Software and application security
    1. Domain-specific security and privacy architectures

Recommendations

Privacy-enhancing technologies: approaches and development

In this paper, we discuss privacy threats on the Internet and possible solutions to this problem. Examples of privacy threats in the communication networks are identity disclosure, linking data traffic with identity, location disclosure in connection ...
Read More
How Privacy Concerns, Trust and Risk Beliefs, and Privacy Literacy Influence Users' Intentions to Use Privacy-Enhancing Technologies: The Case of Tor

Due to an increasing collection of personal data by internet companies and several data breaches, research related to privacy gained importance in the last years in the information systems domain. Privacy concerns can strongly influence users' decision ...
Read More
Collaborative privacy management

The landscape of the World Wide Web with all its versatile services heavily relies on the disclosure of private user information. Unfortunately, the growing amount of personal data collected by service providers poses a significant privacy threat for ...
Read More

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

Published in
Journal on Computing and Cultural Heritage Volume 16, Issue 4
December 2023
473 pages
ISSN:1556-4673
EISSN:1556-4711
DOI:10.1145/3615351
Editor:
Franco Niccolucci
VAST-LAB at PIN, University of Florence, Italy
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
In-Cooperation
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
- Published: 8 January 2024
- Online AM: 27 November 2023
- Accepted: 14 September 2023
- Revised: 25 August 2023
- Received: 5 March 2023
Published in jocch Volume 16, Issue 4

Permissions
Request permissions about this article.
Request Permissions

Check for updates
Author Tags
Privacy-enhancing technologies
homomorphic encryption
trusted execution environments
secure multiparty computation
personal data stores
privacy-preserving machine learning
synthetic data
Qualifiers
- research-article
Conference
Funding Sources
Other Metrics
View Article Metrics

Article Metrics
- 0
  Total Citations
  View Citations
- 463
  Total Downloads
- Downloads (Last 12 months)463
- Downloads (Last 6 weeks)122
Other Metrics
View Author Metrics
Cited By
This publication has not been cited yet

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Full Text

View this article in Full Text.

View Full Text

Protecting Privacy in Digital Records: The Potential of Privacy-Enhancing Technologies

Journal on Computing and Cultural Heritage

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

Privacy-enhancing technologies: approaches and development

How Privacy Concerns, Trust and Risk Beliefs, and Privacy Literacy Influence Users' Intentions to Use Privacy-Enhancing Technologies: The Case of Tor

Collaborative privacy management