Download PDFOpen PDF in browser

Privacy-Preserving Data Aggregation Scheme for E-Health

EasyChair Preprint no. 8820

6 pagesDate: September 6, 2022


E-Health is the use of digital services and communication technology in support of healthcare. E-Health services are becoming increasingly popular. With E-Health, large amounts of data need to be collected, stored, and sent to other places all while remaining private. This raises the need for privacy-preserving data aggregation schemes to be implemented. Many other privacy-preserving data aggregation schemes already exist for E-Health services utilizing tools such as homomorphic encryption which can be slow with large amounts of data. This paper proposes a privacy-preserving scheme to aggregate data in an E-Health setting. Our scheme allows for all of the patients’ individual data to remain private. Doctors can utilize partial decryption in our scheme to collect specific information about patients such as how many patients have high blood pressure without seeing all of the patients’ data.

Keyphrases: aggregation, Encryption, K-Nearest Neighbor

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Matthew Watkins and Colby Dorsey and Daniel Rennier and Timothy Polley and Ahmed Sherif and Mohamed Elsersy},
  title = {Privacy-Preserving Data Aggregation Scheme for E-Health},
  howpublished = {EasyChair Preprint no. 8820},

  year = {EasyChair, 2022}}
Download PDFOpen PDF in browser