Federated Learning with Differential Privacy Via Fast Fourier Transform for Tighter-Efficient Combining
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Spurred by the simultaneous need for data privacy protection and data sharing, federated learning has been proposed. However, there is still a risk of privacy leakage in it. In this paper, an improved differential privacy algorithm is proposed to protect the federated learning model. And at the same time, the Fast Fourier Transform is used in the computation of the privacy budget , to minimize the impact of limited arithmetic resources and numerous users on the effectiveness of the training model. Then, discarding the various discussions that were directly on the privacy budget instead, FFT is used together with PLD in this process for calculating consumption, which further tightens the bound of computation with minimal impact on the efficiency. Moreover, the activation function for model training is improved by using a temper sigmoid with only one parameter , which much smoother the accuracy curve and reduces the drastic fluctuating scenarios. Finally, simulation results on real datasets show that the federated learning with the DP algorithm that considers the long trailing case facilitates better equalizing the relationship between privacy and utility.Keywords:
Differential Privacy
Differential privacy is a formal mathematical framework for quantifying the degree of individual privacy in a statistical database.To guarantee differential privacy, a typical method is to add random noise to the original data for data release. In this paper, we investigate the conditions of differential privacy (single-dimensional case) considering the general random noise adding mechanism, and then apply the obtained results for privacy analysis of the privacy-preserving consensus algorithm. Specifically, we obtain a necessary and sufficient condition of e-differential privacy, and the sufficient conditions of (e, δ)-differential privacy. We apply them to analyze various random noises. For the special cases with known results, our theory not only matches with the literature, but also provides an efficient approach to the privacy parameters' estimation; for other cases that are unknown, our approach provides a simple and effective tool for differential privacy analysis. Applying the obtained theory on privacy-preserving consensus algorithm, we obtain the necessary condition and the sufficient condition to ensure differential privacy.
Differential Privacy
Random noise
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Differential privacy is a formal mathematical {stand-ard} for quantifying the degree of that individual privacy in a statistical database is preserved. To guarantee differential privacy, a typical method is adding random noise to the original data for data release. In this paper, we investigate the conditions of differential privacy considering the general random noise adding mechanism, and then apply the obtained results for privacy analysis of the privacy-preserving consensus algorithm. Specifically, we obtain a necessary and sufficient condition of $ε$-differential privacy, and the sufficient conditions of $(ε, δ)$-differential privacy. We apply them to analyze various random noises. For the special cases with known results, our theory matches with the literature; for other cases that are unknown, our approach provides a simple and effective tool for differential privacy analysis. Applying the obtained theory, on privacy-preserving consensus algorithms, it is proved that the average consensus and $ε$-differential privacy cannot be guaranteed simultaneously by any privacy-preserving consensus algorithm.
Differential Privacy
Random noise
Privacy software
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Privacy is an important guarantee to give to users in order for them to agree to release their, possibly sensitive, data for scientific or commercial purposes. However,
guaranteeing privacy is not a trivial task. Previously there have been several cases where released data was believed to have been anonymized, where it later proved not
to be anonymous at all [28, 38]. One methodology to be able to release anonymized calculations is differential privacy, where controlled noise is added to the calculation
before it is release. However, there exists a trade-off between the privacy and the accuracy of the results when differential privacy is used. Previous work has mostly
focused on differential privacy in theory, but there also exists work that applies differential privacy to a use case [32]. However, the utility of the differentially
private results have not previously been evaluated when using only counting queries. In this thesis differential privacy is applied to one use case found in the smart
grid, an evolved version of the electricity grid, to show that differential privacy is applicable in practice and not only in theory. The particular use case in this thesis compares a differentially private sum to the true sum, to estimate the error introduced by applying differential privacy. The results demonstrate that differential privacy shows promise also for realistic usage, providing privacy while still producing accurate results compared to the true results without differential privacy applied. For a setup with 1,000 simulated households, the best results for the mean error is between 0.42% and 0.59%, and the spread of the error ranged from 0% to 2.07%. All of these results have a confidence interval of 95%.
Differential Privacy
Privacy software
Privacy Protection
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Differential privacy is a formal mathematical standard for quantifying the degree of that individual privacy in a statistical database is preserved. To guarantee differential privacy, a typical method is adding random noise to the original data for data release. In this paper, we investigate the basic conditions of differential privacy considering the general random noise adding mechanism, and then apply this result for privacy analysis of the privacy-preserving consensus algorithm. Specifically, we obtain a necessary and sufficient condition of differential privacy, which provides a useful and efficient criterion of achieving differential privacy. We utilize the result to analyze the privacy of some common random noises and the theory matches with the existing literature for special cases. Applying the theory, differential privacy property of a privacy-preserving consensus algorithm is investigated based on the proposed theory. We obtain the necessary condition of differential privacy for the privacy-preserving consensus algorithm. In addition, it is proved that the average consensus and differential privacy cannot be guaranteed simultaneously by any privacy-preserving consensus algorithm.
Differential Privacy
Privacy software
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Differential privacy is rigorous framework for stating and enforcing privacy guarantees on computations over sensitive data. Informally, differential privacy ensures that the presence or absence of a single individual in a database has only a negligible statistical effect on the computation's result. Many specific algorithms have been proved differentially private, but manually checking that a given program is differentially private can be subtle, tedious, or both. This approach becomes unfeasible when larger programs are considered.
Differential Privacy
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In the past decade analysis of big data has proven to be extremely valuable in many contexts. Local Differential Privacy (LDP) is a state-of-the-art approach which allows statistical computations while protecting each individual user's privacy. Unlike Differential Privacy no trust in a central authority is necessary as noise is added to user inputs locally. In this paper we give an overview over different LDP algorithms for problems such as locally private heavy hitter identification and spatial data collection. Finally, we will give an outlook on open problems in LDP.
Differential Privacy
Identification
Privacy software
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Differential privacy is a formal mathematical standard for quantifying the degree of that individual privacy in a statistical database is preserved. To guarantee differential privacy, a typical method is adding random noise to the original data for data release. In this paper, we investigate the fundamental theory of differential privacy considering the general random noise adding mechanism, and then apply this framework for privacy analysis of the privacy-preserving consensus algorithm. Specifically, we obtain a necessary and sufficient condition of $\epsilon$-differential privacy, and the sufficient conditions of $(\epsilon, \delta)$-differential privacy. This theoretical framework provides a useful and efficient criterion of achieving differential privacy. We utilize them to analyze the privacy of some common random noises and the theory matches with the existing literature for special cases. Applying the theory, differential privacy property of a privacy-preserving consensus algorithm is investigated based on the framework. We obtain the necessary condition and the sufficient condition for the privacy-preserving consensus algorithm, under which differential privacy is achieved, respectively. In addition, it is proved that the average consensus and $\epsilon$-differential privacy cannot be guaranteed simultaneously by any privacy-preserving consensus algorithm.
Differential Privacy
Privacy software
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Differential Privacy
Similarity (geometry)
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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