Results 51 to 60 of about 29,077 (283)
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
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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 +2 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 +2 more
core +1 more source
K-Means Clustering with Local Distance Privacy
Big Data Mining and Analytics, 2023 With the development of information technology, a mass of data are generated every day. Collecting and analysing these data help service providers improve their services and gain an advantage in the fierce market competition.
Mengmeng Yang +2 more
doaj +1 more source
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
Local differential privacy-based federated learning for Internet of Things
, 2021 The Internet of Vehicles (IoV) is a promising branch of the Internet of Things. IoV simulates a large variety of crowdsourcing applications such as Waze, Uber, and Amazon Mechanical Turk, etc.
Lyu, Lingjuan +7 more
core +1 more source
LDPORR: A localized location privacy protection method based on optimized random response
Journal of King Saud University: Computer and Information Sciences, 2023 The broad use of mobile intelligent terminals with locating functions encourages the rapid development of location-based services (LBS), which are widely used in a variety of industries such as social networking, transportation, finance, and ...
Yan Yan +4 more
doaj +1 more source
On the Lift, Related Privacy Measures, and Applications to Privacy–Utility Trade-Offs
Entropy, 2023 This paper investigates lift, the likelihood ratio between the posterior and prior belief about sensitive features in a dataset. Maximum and minimum lifts over sensitive features quantify the adversary’s knowledge gain and should be bounded to protect ...
Mohammad Amin Zarrabian +2 more
doaj +1 more source
Separating Local & Shuffled Differential Privacy via Histograms [PDF]
, 2020 Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands.
Cheu, Albert, Balcer, Victor
core +1 more source