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Jianting Ning

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13 records found

Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

Journal article (2025) - Pengfei Wu, Jianting Ning, Xinyi Huang, Rongmao Chen, Kai Zhang, Kaitai Liang
Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this article, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ...
Journal article (2022) - Jianting Ning, Xinyi Huang, Willy Susilo, Kaitai Liang, Ximeng Liu, Yinghui Zhang
Cloud-based data storage service has drawn increasing interests from both academic and industry in the recent years due to its efficient and low cost management. Since it provides services in an open network, it is urgent for service providers to make use of secure data storage and sharing mechanism to ensure data confidentiality and service user privacy. To protect sensitive data from being compromised, the most widely used method is encryption. However, simply encrypting data (e.g., via AES) cannot fully address the practical need of data management. Besides, an effective access control over download request also needs to be considered so that Economic Denial of Sustainability (EDoS) attacks cannot be launched to hinder users from enjoying service. In this article, we consider the dual access control, in the context of cloud-based storage, in the sense that we design a control mechanism over both data access and download request without loss of security and efficiency. Two dual access control systems are designed in this article, where each of them is for a distinct designed setting. The security and experimental analysis for the systems are also presented. ...

Volume and Access Pattern Leakage-Abuse Attack with Leaked Documents

Conference paper (2022) - Steven Lambregts, Huanhuan Chen, Jianting Ning, Kaitai Liang
Searchable Encryption schemes provide secure search over encrypted databases while allowing admitted information leakages. Generally, the leakages can be categorized into access and volume pattern. In most existing SE schemes, these leakages are caused by practical designs but are considered an acceptable price to achieve high search efficiency. Recent attacks have shown that such leakages could be easily exploited to retrieve the underlying keywords for search queries. Under the umbrella of attacking SE, we design a new Volume and Access Pattern Leakage-Abuse Attack (VAL-Attack) that improves the matching technique of LEAP (CCS ’21) and exploits both the access and volume patterns. Our proposed attack only leverages leaked documents and the keywords present in those documents as auxiliary knowledge and can effectively retrieve document and keyword matches from leaked data. Furthermore, the recovery performs without false positives. We further compare VAL-Attack with two recent well-defined attacks on several real-world datasets to highlight the effectiveness of our attack and present the performance under popular countermeasures. ...
Journal article (2021) - Hangyu Zhu, Rui Wang, Yaochu Jin, Kaitai Liang, Jianting Ning
Homomorphic encryption is a very useful gradient protection technique used in privacy preserving federated learning. However, existing encrypted federated learning systems need a trusted third party to generate and distribute key pairs to connected participants, making them unsuited for federated learning and vulnerable to security risks. Moreover, encrypting all model parameters is computationally intensive, especially for large machine learning models such as deep neural networks. In order to mitigate these issues, we develop a practical, computationally efficient encryption based protocol for federated deep learning, where the key pairs are collaboratively generated without the help of a trusted third party. By quantization of the model parameters on the clients and an approximated aggregation on the server, the proposed method avoids encryption and decryption of the entire model. In addition, a threshold based secret sharing technique is designed so that no one can hold the global private key for decryption, while aggregated ciphertexts can be successfully decrypted by a threshold number of clients even if some clients are offline. Our experimental results confirm that the proposed method significantly reduces the communication costs and computational complexity compared to existing encrypted federated learning without compromising the performance and security. ...
Journal article (2020) - Yi Zhao, Jianting Ning, Kaitai Liang, Yanqi Zhao, Liqun Chen, Bo Yang
Searchable functionality is provided in many online services such as mail services or outsourced data storage. To protect users privacy, data in these services is usually stored after being encrypted using searchable encryption. This enables the data user to securely search encrypted data from a remote server without leaking data and query information. Public key encryption with keyword search is one of the research branches of searchable encryption; this provides privacy-preserving searchable functionality for applications such as encrypted email systems. However, it has an inherent vulnerability in that the information of a query may be leaked using a keyword guessing attack. Most of existing works aim to make the system resistant to offline keyword guessing, but this does not protect against online attacks on real world services. In this paper, we move a step forward to present a generic framework able to resist online keyword guessing attack using a server-assisted model. Specifically, we design a novel primitive C mirrored all-but-one lossy encryption, which can prevent a specific user from generating valid encryptions. This primitive can be seen as an access control on encryption ability. Combining searchable encryption technique with the new primitive makes online keyword guessing attack impossible for the specified user, even if the attack is launched online. We further give formal security analysis for the generic framework, and a concrete implementation with efficiency analysis to show that our design is practical. ...
Journal article (2020) - Jianting Ning, Jiageng Chen, Kaitai Liang, Joseph K. Liu, Chunhua Su, Qianhong Wu
Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Liang et al. proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency, O(n d)O(nd), where dd is the total number of system files and nn is the size of query formula. This article, for the first time, leverages the 'oblivious cross search' technology in public key searchable encryption context to reduce the search complexity to only O(nf(w))O(nf(w)), where f(w)f(w) is the number of files embedded with the 'least frequent keyword' ww. The new scheme maintains efficient encrypted data share and keyword field update as well. This article further revisits the security models for payload security, keyword privacy and search token privacy (i.e., search pattern privacy) and meanwhile, presents security and efficiency analysis for the new scheme. ...
Journal article (2020) - Jiahui Chen, Jie Ling, Jianting Ning, Emmanouil Panaousis, George Loukas, Kaitai Liang, Jiageng Chen
Journal article (2019) - Jianting Ning, Jia Xu, Kaitai Liang, Fan Zhang, Ee-Chien Chang
Journal article (2018) - Man Ho Au, Kaitai Liang, Joseph K. Liu, Rongxing Lu, Jianting Ning
Journal article (2018) - Jianting Ning, Zhenfu Cao, Xiaolei Dong, Kaitai Liang, Hui Ma, Lifei Wei

Secure and Expressive Data Access Control for Cloud Storage

Journal article (2018) - Jianting Ning, Zhenfu Cao, Xiaolei Dong, Kaitai Liang, Lifei Wei, Kim Kwang Raymond Choo
Secure cloud storage, an emerging cloud service, guarantees the confidentiality of outsourced data while providing flexible data access control for cloud users whose data are out of their physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the promising secure mechanisms to support fine-grained access control on encrypted data in cloud settings. However, due to its inherent "all-or-nothing" decryption control characteristic, there is a risk for the misuse of access credentials. In this paper, we consider the two main types of access credential misuse, namely: semi-trusted authority's illegal access credential (re-)distribution, and cloud user's illegal access credential leakage. To mitigate these two types of access credential misuse, we propose the first accountable authority revokable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud+. We also prove the security of our system and present the experimental results to demonstrate the utility of our system. ...

Securely Switch Your Encrypted Data to Another Format

Journal article (2018) - Peng Jiang, Jianting Ning, Kaitai Liang, Changyu Dong, Jiageng Chen, Zhenfu Cao
Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under different encryption mechanisms when different data owners try to secure their data. This makes data aggregation very difficult, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching that bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme is applicable in multi-domain cloud storage system. ...
Book chapter (2017) - Ru Meng, Yanwei Zhou, Jianting Ning, Kaitai Liang, Jinguang Han, Willy Susilo