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ردیف | عنوان | نوع |
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1 |
Obtaining accurate estimated action values in categorical distributional reinforcement learning
بدست آوردن مقادیر دقیق عمل تخمینی در یادگیری تقویتی توزیعی دسته بندی شده-2020 Categorical Distributional Reinforcement Learning (CDRL) uses a categorical distribution with evenly
spaced outcomes to model the entire distribution of returns and produces state-of-the-art empirical
performance. However, using inappropriate bounds with CDRL may generate inaccurate estimated
action values, which affect the policy update step and the final performance. In CDRL, the bounds of
the distribution indicate the range of the action values that the agent can obtain in one task, without
considering the policy’s performance and state–action pairs. The action values that the agent obtains
are often far from the bounds, and this reduces the accuracy of the estimated action values. This
paper describes a method of obtaining more accurate estimated action values for CDRL using adaptive
bounds. This approach enables the bounds of the distribution to be adjusted automatically based on
the policy and state–action pairs. To achieve this, we save the weights of the critic network over a fixed
number of time steps, and then apply a bootstrapping method. In this way, we can obtain confidence
intervals for the upper and lower bound, and then use the upper and lower bound of these intervals as
the new bounds of the distribution. The new bounds are more appropriate for the agent and provide a
more accurate estimated action value. To further correct the estimated action values, a distributional
target policy is proposed as a smoothing method. Experiments show that our method outperforms
many state-of-the-art methods on the OpenAI gym tasks. Keywords: Distributional reinforcement learning | Estimated action value | Bootstrapping | Interval estimation |
مقاله انگلیسی |
2 |
Scalable and secure access control policy update for outsourced big data
بروزرسانی سیاست کنترل دسترسی مقیاس پذیر و امن برای داده های بزرگ برون سپاری شده-2018 Ciphertext Policy Attribute-based Encryption (CP-ABE) is proven to be one of the most effective ap
proaches to data access control in data outsourcing environment such as cloud computing since it provides
efficient key management based on user attributes of multiple users in accessing shared data. However,
dealing with policy update limits the efficiency of the CP-ABE. In CP-ABE scheme, the access policy is used
as a core element for data encryption. Hence, if the policy is updated, the data owner needs to re-encrypt
files and send them back to the cloud. This incurs overheads including computation, communication, and
maintenance cost at the data owner side. The computation and communication cost are very expensive
if the outsourcing environment is devoted to ‘‘big data’’. In this paper, we extend the capability of our
access control scheme: C-CP-ARBE to be capable of supporting secure and flexible policy updates in the
big data outsourcing environment. We develop a secure policy updating algorithm and propose a very
lightweight proxy re-encryption (VL-PRE) technique to enable the policy updating to be done in the cloud
in an efficient and computationally cost effective manner. Finally, we demonstrate the efficiency and
performance of our proposed scheme through the implementation.
Keywords: CP-ABE ، Data access control ، Cloud computing ، Policy update ، Proxy re-encryption |
مقاله انگلیسی |
3 |
Privacy-preserving fusion of IoT and big data for e-health
همگام سازی حفظ حریم شخصی اینترنت اشیا و داده های بزرگ برای سلامتی الکترونیکی-2018 In this paper, we propose a privacy-preserving e-health system, which is a fusion of Internet-of-things
(IoT), big data and cloud storage. The medical IoT network monitors patient’s physiological data, which
are aggregated to electronic health record (EHR). The medical big data that contains a large amount of
EHRs are outsourced to cloud platform. In the proposed system, the patient distributes an IoT group key
to the medical nodes in an authenticated way without interaction round. The IoT messages are encrypted
using the IoT group key and transmitted to the patient, which can be batch authenticated by the patient.
The encrypted EHRs are shared among patient and different data users in a fine-grained access control
manner. A novel keyword match based policy update mechanism is designed to enable flexible access
policy updating without privacy leakage. Extensive comparison and simulation results demonstrate that
the algorithms in the proposed system are efficient. Comprehensive analysis is provided to prove its
security.
Keywords: Internet-of-things ، Big data ، Privacy-preserving ، Cloud storage ، Access control ، Keyword match based policy update |
مقاله انگلیسی |
4 |
A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds
یک طرح کنترل دسترسی امن و قابل بررسی برای ذخیره سازی داده های بزرگ در ابرها-2017 Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches
for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud
based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical
and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party
authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join
and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big
data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU,
and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud
server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the
update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the
legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext
recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the
collusion attack.
Index Terms: Big Data Storage | Access Control | the NTRU Cryptosystem | Secret Sharing | Access Policy Update | Cloud Computing |
مقاله انگلیسی |