Results 41 to 50 of about 7,877,596 (288)
Fisher Information Under Local Differential Privacy [PDF]
IEEE Journal on Selected Areas in Information Theory, 2020 We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter $\varepsilon$ under local differential privacy constraints. These bounds are valid under general conditions on the distribution of the score of the statistical model, and they elucidate under which conditions the ...
Leighton Pate Barnes +2 more
openaire +2 more sources
Local Differential Privacy for Deep Learning
IEEE Internet of Things Journal, 2020 The internet of things (IoT) is transforming major industries including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations such as the amalgamation of software-defined networks (SDN) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning
Mahawaga Arachchige Pathum Chamikara +5 more
openaire +4 more sources
The Role of Interactivity in Local Differential Privacy [PDF]
2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), 2019 We study the power of interactivity in local differential privacy. First, we focus on the difference between fully interactive and sequentially interactive protocols. Sequentially interactive protocols may query users adaptively in sequence, but they cannot return to previously queried users.
Matthew Joseph +3 more
openaire +4 more sources
(Local) Differential Privacy has NO Disparate Impact on Fairness
, 2023 This paper received the Best Paper Award at DBSec 2023International audienceIn recent years, Local Differential Privacy (LDP), a robust privacy-preserving methodology, has gained widespread adoption in realworld applications.
Arcolezi, Héber Hwang +3 more
core +1 more source
Behavior Sequence Mining Model Based on Local Differential Privacy
IEEE Access, 2020 Most of local differential privacy frameworks target statistics on certain privacy behaviors of users, but not behavior sequence. In this paper, we explore and propose a behavior sequence mining model that satisfies the local differential privacy ...
Jianen Yan, Yan Wang, Wenling Li
doaj +1 more source
Fldp: Flexible Strategy For Local Differential Privacy
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Local differential privacy (LDP), a technique applying unbiased statistical estimations instead of real data, is often adopted in data collection. In particular, this technique is used with frequency oracles (FO) because it can protect each user's privacy and prevent leakage of sensitive information.
Dan Zhao 0009 +5 more
openaire +4 more sources
Local Node Differential Privacy
CoRRWe initiate an investigation of node differential privacy for graphs in the local model of private data analysis. In our model, dubbed LNDP*, each node sees its own edge list and releases the output of a local randomizer on this input. These outputs are aggregated by an untrusted server to obtain a final output.
Sofya Raskhodnikova +3 more
openaire +2 more sources
Randori: Local Differential Privacy for All
CoRR, 2021 Polls are a common way of collecting data, including product reviews and feedback forms. However, few data collectors give upfront privacy guarantees. Additionally, when privacy guarantees are given upfront, they are often vague claims about 'anonymity'.
openaire +2 more sources
Privacy-Preserving Payment System With Verifiable Local Differential Privacy
, 2023 Privacy-preserving transaction systems on blockchain networks like Monero or Zcash provide complete transaction anonymity through cryptographic commitments or encryption.
Eran Toch +2 more
core +1 more source
Exponential Separations in Local Differential Privacy [PDF]
, 2020 We prove a general connection between the communication complexity of two-player games and the sample complexity of their multi-player locally private analogues. We use this connection to prove sample complexity lower bounds for locally differentially private protocols as straightforward corollaries of results from communication complexity.
Matthew Joseph +2 more
openaire +2 more sources