Author Login IJIRT Partner Network (IPN) Login

Secure Data Collaboration Using Privacy-Preserving Technologies

  • Unique Paper ID: 181925
  • Volume: 12
  • Issue: 2
  • PageNo: 87-97
  • Abstract:
  • The proliferation of data-driven business models has led to significant privacy and intellectual property (IP) issues, especially for early-stage startups relying on user data and new sources of insights. Privacy-preserving technologies (PPTs) such as federated learning (FL), differential privacy (DP), homo- morphic encryption (HE), and secure multi-party computation (SMPC), enable technical processes for data utilization while preserving the individual’s privacy and IP. The purpose of this paper is to examine the current state of PPTs and provide a conceptual framework for their role in startup settings. The paper draws on the latest literature on the topic and shows how FL and DP allow for collaborative training of models between organizations without the sharing of centralized data [1], [6], how HE enables computations to be performed on encrypted data [4], [13], and the means in which SMPC can build analytics without sharing the private inputs [16], [18]. We then assessed their applications in startup ecosystems and IP management with situations such as collaborative product development and decentralized data marketplaces where FL or HE could be leveraged to extract value from mutual insights without sharing confidential data or trade secrets [8], [20]. Finally, we researched the legal and economic motivations for adopting the technology such as fulfillment of GDPR and CCPA requirements which contributed to adoption by emerging companies [7], [12]. We illustrate how PPTs enable the opportunity for users to build trust and have more regulatory alignment to committing to privacy- centric systems [14], [15]. Practical cases, such as healthcare analytics or monetizing genomic data demonstrate the tradeoffs between privacy, utility, and computational costs [5], [19]. Finally, we note the remaining challenges (e.g., technical scalability, regulatory ambiguity, and economic disincentives), and outline promising future directions (e.g., regulatory standardization, ethical design, and best practices for specific industries) [2], [3], [10]. Overall, we show how PPTs can enable innovation while reinforcing privacy protection for both customers and the firms’ intellectual property.
Download article email to a friend

Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 2
  • PageNo: 87-97

Secure Data Collaboration Using Privacy-Preserving Technologies

Related Articles

Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Archive

View More

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry