A survey and position paper mapping privacy threats in mental health AI and recommending a pipeline of anonymization, synthetic data, and differential privacy.
Parameter Estimation in a Noisy 1D Environment via Two Absorbing Receivers
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abstract
This paper investigates the estimation of different parameters, e.g., propagation distance and flow velocity, by utilizing two fully-absorbing receivers (RXs) in a one-dimensional (1D) environment. The time-varying number of absorbed molecules at each RX and the number of absorbed molecules in a time interval as time approaches infinity are derived. Noisy molecules in this environment, that are released by sources in addition to the transmitter, are also considered. A novel estimation method, namely difference estimation (DE), is proposed to eliminate the effect of noise by using the difference of received signals at the two RXs. For DE, the Cramer-Rao lower bound (CRLB) on the variance of estimation is derived. Independent maximum likelihood estimation is also considered at each RX as a benchmark to show the performance advantage of DE. Aided by particle-based simulation, the derived analytical results are verified. Furthermore, numerical results show that DE attains the CRLB and is less sensitive to the change of noise than independent estimation at each RX.
fields
cs.CL 1years
2025 1verdicts
ACCEPT 1representative citing papers
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Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities
A survey and position paper mapping privacy threats in mental health AI and recommending a pipeline of anonymization, synthetic data, and differential privacy.