TY - GEN
T1 - DPLE
T2 - 9th IEEE International Conference on Smart Cloud, SmartCloud 2024
AU - Xue, Yilei
AU - Li, Jianhua
AU - Wu, Jun
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Distributed machine learning (DML) encounters issues related to privacy and the presence of straggling nodes in smart cloud systems. Lagrange coded computing offers a partial solution to mitigate these concerns. Nonetheless, the privacy of the system becomes vulnerable when the number of semi-trusted nodes surpasses a specific limit, or when external eavesdroppers are present. To confront this hurdle, we introduce a novel framework for distributed learning called DPLE (Differentially Private Lagrange Encoding). This framework employs Lagrange interpolation polynomials to obscure the original data while introducing redundancy, thus improving privacy safeguards and increasing robustness to straggling nodes. It also incorporates artificial noise into local computation outcomes to protect confidential data from potential exposures. Furthermore, we perform theoretical analyses to identify the necessary variance of this noise to maintain desired privacy levels. Experimental validations confirm the efficacy of DPLE and examine how different settings of system parameters impact the accuracies of the results.
AB - Distributed machine learning (DML) encounters issues related to privacy and the presence of straggling nodes in smart cloud systems. Lagrange coded computing offers a partial solution to mitigate these concerns. Nonetheless, the privacy of the system becomes vulnerable when the number of semi-trusted nodes surpasses a specific limit, or when external eavesdroppers are present. To confront this hurdle, we introduce a novel framework for distributed learning called DPLE (Differentially Private Lagrange Encoding). This framework employs Lagrange interpolation polynomials to obscure the original data while introducing redundancy, thus improving privacy safeguards and increasing robustness to straggling nodes. It also incorporates artificial noise into local computation outcomes to protect confidential data from potential exposures. Furthermore, we perform theoretical analyses to identify the necessary variance of this noise to maintain desired privacy levels. Experimental validations confirm the efficacy of DPLE and examine how different settings of system parameters impact the accuracies of the results.
KW - Distributed machine learning
KW - Lagrange interpolation polynomial
KW - coded computing
KW - differential privacy
UR - http://www.scopus.com/inward/record.url?scp=85198021648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85198021648&partnerID=8YFLogxK
U2 - 10.1109/SmartCloud62736.2024.00009
DO - 10.1109/SmartCloud62736.2024.00009
M3 - Conference contribution
AN - SCOPUS:85198021648
T3 - Proceedings - 2024 IEEE 9th International Conference on Smart Cloud, SmartCloud 2024
SP - 7
EP - 12
BT - Proceedings - 2024 IEEE 9th International Conference on Smart Cloud, SmartCloud 2024
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 May 2024 through 12 May 2024
ER -