Visible to the public Biblio

Filters: Author is Chen, Hao  [Clear All Filters]
2020-01-21
Liu, Yi, Dong, Mianxiong, Ota, Kaoru, Wu, Jun, Li, Jianhua, Chen, Hao.  2019.  SCTD: Smart Reasoning Based Content Threat Defense in Semantics Knowledge Enhanced ICN. ICC 2019 - 2019 IEEE International Conference on Communications (ICC). :1–6.
Information-centric networking (ICN) is a novel networking architecture with subscription-based naming mechanism and efficient caching, which has abundant semantic features. However, existing defense studies in ICN fails to isolate or block efficiently novel content threats including malicious penetration and semantic obfuscation for the lack of researches considering ICN semantic features. More importantly, to detect potential threats, existing security works in ICN fail to use semantic reasoning to construct security knowledge-based defense mechanism. Thus ICN needs a smart and content-based defense mechanism. Current works are not able to block content threats implicated in semantics. Additionally, based on traditional computing resources, they are incompatible with ICN protocols. In this paper, we propose smart reasoning based content threat defense for semantics knowledge enhanced ICN. A fog computing based defense mechanism with content semantic awareness is designed to build ICN edge defense system. In addition, smart reasoning algorithms is proposed to detect implicit knowledge and semantic relations in packet names and contents with context communication content and knowledge graph. On top of inference knowledge, the mechanism can perceive threats from ICN interests. Simulations demonstrate the validity and efficiency of the proposed mechanism.
2019-12-30
Chen, Hao, Huang, Zhicong, Laine, Kim, Rindal, Peter.  2018.  Labeled PSI from Fully Homomorphic Encryption with Malicious Security. Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security. :1223–1237.
Private Set Intersection (PSI) allows two parties, the sender and the receiver, to compute the intersection of their private sets without revealing extra information to each other. We are interested in the unbalanced PSI setting, where (1) the receiver's set is significantly smaller than the sender's, and (2) the receiver (with the smaller set) has a low-power device. Also, in a Labeled PSI setting, the sender holds a label per each item in its set, and the receiver obtains the labels from the items in the intersection. We build upon the unbalanced PSI protocol of Chen, Laine, and Rindal (CCS\textbackslashtextasciitilde2017) in several ways: we add efficient support for arbitrary length items, we construct and implement an unbalanced Labeled PSI protocol with small communication complexity, and also strengthen the security model using Oblivious Pseudo-Random Function (OPRF) in a pre-processing phase. Our protocols outperform previous ones: for an intersection of 220 and \$512\$ size sets of arbitrary length items our protocol has a total online running time of just \$1\$\textbackslashtextasciitildesecond (single thread), and a total communication cost of 4 MB. For a larger example, an intersection of 228 and 1024 size sets of arbitrary length items has an online running time of \$12\$ seconds (multi-threaded), with less than 18 MB of total communication.
2019-11-25
Cui, Hongyan, Chen, Zunming, Xi, Yu, Chen, Hao, Hao, Jiawang.  2019.  IoT Data Management and Lineage Traceability: A Blockchain-based Solution. 2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops). :239–244.
The Internet of Things is stepping out of its infancy into full maturity, requiring massive data processing and storage. Unfortunately, because of the unique characteristics of resource constraints, short-range communication, and self-organization in IoT, it always resorts to the cloud or fog nodes for outsourced computation and storage, which has brought about a series of novel challenging security and privacy threats. For this reason, one of the critical challenges of having numerous IoT devices is the capacity to manage them and their data. A specific concern is from which devices or Edge clouds to accept join requests or interaction requests. This paper discusses a design concept for developing the IoT data management platform, along with a data management and lineage traceability implementation of the platform based on blockchain and smart contracts, which approaches the two major challenges: how to implement effective data management and enrich rational interoperability for trusted groups of linked Things; And how to settle conflicts between untrusted IoT devices and its requests taking into account security and privacy preserving. Experimental results show that the system scales well with the loss of computing and communication performance maintaining within the acceptable range, works well to effectively defend against unauthorized access and empower data provenance and transparency, which verifies the feasibility and efficiency of the design concept to provide privacy, fine-grained, and integrity data management over the IoT devices by introducing the blockchain-based data management platform.
2018-01-16
Chen, Hao, Laine, Kim, Rindal, Peter.  2017.  Fast Private Set Intersection from Homomorphic Encryption. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1243–1255.

Private Set Intersection (PSI) is a cryptographic technique that allows two parties to compute the intersection of their sets without revealing anything except the intersection. We use fully homomorphic encryption to construct a fast PSI protocol with a small communication overhead that works particularly well when one of the two sets is much smaller than the other, and is secure against semi-honest adversaries. The most computationally efficient PSI protocols have been constructed using tools such as hash functions and oblivious transfer, but a potential limitation with these approaches is the communication complexity, which scales linearly with the size of the larger set. This is of particular concern when performing PSI between a constrained device (cellphone) holding a small set, and a large service provider (e.g. WhatsApp), such as in the Private Contact Discovery application. Our protocol has communication complexity linear in the size of the smaller set, and logarithmic in the larger set. More precisely, if the set sizes are Ny textless Nx, we achieve a communication overhead of O(Ny log Nx). Our running-time-optimized benchmarks show that it takes 36 seconds of online-computation, 71 seconds of non-interactive (receiver-independent) pre-processing, and only 12.5MB of round trip communication to intersect five thousand 32-bit strings with 16 million 32-bit strings. Compared to prior works, this is roughly a 38–115x reduction in communication with minimal difference in computational overhead.

2017-05-16
Stevens, Ryan, Crussell, Jonathan, Chen, Hao.  2016.  On the Origin of Mobile Apps: Network Provenance for Android Applications. Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy. :160–171.

Many mobile services consist of two components: a server providing an API, and an application running on smartphones and communicating with the API. An unresolved problem in this design is that it is difficult for the server to authenticate which app is accessing the API. This causes many security problems. For example, the provider of a private network API has to embed secrets in its official app to ensure that only this app can access the API; however, attackers can uncover the secret by reverse-engineering. As another example, malicious apps may send automatic requests to ad servers to commit ad fraud. In this work, we propose a system that allows network API to authenticate the mobile app that sends each request so that the API can make an informed access control decision. Our system, the Mobile Trusted-Origin Policy, consists of two parts: 1) an app provenance mechanism that annotates outgoing HTTP(S) requests with information about which app generated the network traffic, and 2) a code isolation mechanism that separates code within an app that should have different app provenance signatures into mobile origin. As motivation for our work, we present two previously-unknown families of apps that perform click fraud, and examine how the lack of mobile origin information enables the attacks. Based on our observations, we propose Trusted Cross-Origin Requests to handle point (1), which automatically includes mobile origin information in outgoing HTTP requests. Servers may then decide, based on the mobile origin data, whether to process the request or not. We implement a prototype of our system for Android and evaluate its performance, security, and deployability. We find that our system can achieve our security and utility goals with negligible overhead.