Na Ruan

When CSI Meets Public WiFi: Inferring Your Mobile Phone Password via WiFi Signals

In this study, we present WindTalker, a novel and practical keystroke inference framework that allows an attacker to infer the sensitive keystrokes on a mobile device through WiFi-based side-channel information. WindTalker is motivated from the observation that keystrokes on mobile devices will lead to different hand coverage and the finger motions, which will introduce a unique interference to the multi-path signals and can be reflected by the channel state information (CSI). The adversary can exploit the strong correlation between the CSI fluctuation and the keystrokes to infer the users number input. WindTalker presents a novel approach to collect the targets CSI data by deploying a public WiFi hotspot. Compared with the previous keystroke inference approach, WindTalker neither deploys external devices close to the target device nor compromises the target device. Instead, it utilizes the public WiFi to collect users CSI data, which is easy-to-deploy and difficult-to-detect. In addition, it jointly analyzes the traffic and the CSI to launch the keystroke inference only for the sensitive period where password entering occurs. WindTalker can be launched without the requirement of visually seeing the smart phone users input process, backside motion, or installing any malware on the tablet. We implemented Windtalker on several mobile phones and performed a detailed case study to evaluate the practicality of the password inference towards Alipay, the largest mobile payment platform in the world. The evaluation results show that the attacker can recover the key with a high successful rate.

Privacy-Preserving Selective Aggregation of Online User Behavior Data

Tons of online user behavior data are being generated every day on the booming and ubiquitous Internet. Growing efforts have been devoted to mining the abundant behavior data to extract valuable information for research purposes or business interests. However, online users’ privacy is thus under the risk of being exposed to third-parties. The last decade has witnessed a body of research works trying to perform data aggregation in a privacy-preserving way. Most of existing methods guarantee strong privacy protection yet at the cost of very limited aggregation operations, such as allowing only summation, which hardly satisfies the need of behavior analysis. In this paper, we propose a scheme PPSA, which encrypts users’ sensitive data to prevent privacy disclosure from both outside analysts and the aggregation service provider, and fully supports selective aggregate functions for online user behavior analysis while guaranteeing differential privacy. We have implemented our method and evaluated its performance using a trace-driven evaluation based on a real online behavior dataset. Experiment results show that our scheme effectively supports both overall aggregate queries and various selective aggregate queries with acceptable computation and communication overheads.

SPFM: Scalable and Privacy-Preserving Friend Matching in Mobile Cloud

Profile (e.g., contact list, interest, and mobility) matching is more than important for fostering the wide use of mobile social networks. The social networks such as Facebook, Line, or WeChat recommend the friends for the users based on users personal data such as common contact list or mobility traces. However, outsourcing users’ personal information to the cloud for friend matching will raise a serious privacy concern due to the potential risk of data abusing. In this paper, we propose a novel scalable and privacy-preserving friend matching (SPFM) protocol, which aims to provide a scalable friend matching and recommendation solutions without revealing the users personal data to the cloud. Different from the previous works which involves multiple rounds of protocols, SPFM presents a scalable solution which can prevent honest-but-curious mobile cloud from obtaining the original data and support the friend matching of multiple users simultaneously. We give detailed feasibility and security analysis on SPFM and its accuracy and security have been well demonstrated via extensive simulations. The result show that our scheme works even better when original data is large.

Toward Optimal DoS-Resistant Authentication in Crowdsensing Networks via Evolutionary Game

With the increasing demand of Quality of Service(QoS) in Crowdsensing Networks, providing broadcast authentication and preventing Denial of Service (DoS) attacks become not only a fundamental issue but also a challenging security service. The multi-level TESLA is a series of lightweight broadcast authentication protocols, which can effectively mitigate DoS attacks via randomly selected messages. However, the rule of the parameter selection still remains a problem. In this paper, we formulate the attack-defense model as an evolutionary game accordingly, and then present an optimal solution, which achieves security assurance along with minimum resource cost. We then analyze the stability of our evolutionary strategy theoretically. Simulation results are given to evaluate the performance of the proposed algorithm under low QoS channels and severe DoS attacks, which demonstrates that our proposed protocol canworks even in the extreme case.

Efficient and enhanced broadcast authentication protocols based on multilevel μTESLA

Providing lightweight authentication and resisting Denial of Service (DoS) attacks are challenging problems in wireless ad hoc networks, such as wireless sensor networks (WSNs). We introduce two improved protocols based on fault-tolerant protocol and DoS-resistant protocol in Multilevel μTESLA to overcome these difficulties. The proposed Efficient Fault-Tolerant Protocol contributes in shortening the recovery time when highlevel packets are lost, and hence reduces the risk of memory-based DoS attacks. The proposed Enhanced DoS-Resistant Protocol enhances the resistance to DoS attacks by offering packet-loss recovery of authentication message.

De-anonymizing social networks: Using user interest as a side-channel

Social networks, such as Twitter, Instagram, Facebook, and Weibo, have covered a wide range of population throughout the world. People use multiple social networks to enjoy various services as well as share their personal information according to different privacy level. Unlike Facebook and Wechat, in which social connection is more like acquaintance, social networks like Twitter, Instagram, and Weibo represent the social networks in which social link represents interest rather than acquaintance. According to this observation, we propose a community detection method based on interest group, then apply de-anonymization algorithm based on this community. Our experiment shows that this achieves better accuracy than existing de-anonymization algorithm. We conduct several experiments to demonstrate the effect of parameters used in the de-anonymization algorithm.

SmartSec: Secret sharing-based location-aware privacy enhancement in smart devices

In the recent past we have witnessed a quick rise of smart devices, including smart phones, tablets, smart watch, smart television as well as other smart devices. The smart devices store quite a lot of sensitive personal data (e.g., private photos). These devices can breach ones privacy if information in the devices are accessed by unauthorized users. Therefore, smart devices are facing threats of privacy leaking and how to protect privacy of smart devices is regarded as an important research challenge. In this paper, we propose a system, coined as SmartSec, which employs secret sharing techniques among multiple smart devices to enhance privacy protection. In particular, SmartSec can achieve privacy enhancement by storing sensitive data (e.g., photos) on at least three devices and requiring at least two of them to access the original data. This will thus secure the data from any unauthorized attackers because any leakage of one piece of secret is meaningless. Further, secret sharing will introduce extra computation and transmission overhead. To reduce such an overhead and optimize the performance, another important feature of SmartSec is that it could recognize and classify different locations by exploiting location tags and then apply different security policy to different locations to achieve the tradeoff of security and performance. For example, a user can demarcate safe regions (e.g., home area) and the sensitive data will be automatically recovered in home region, distributed and stored/masked at different devices in public regions, where the process is automated which provides the user a transparent experience. We implement the system and conducted a series of experiments on the prototype to evaluate the results.

Privacy-Preserving Mining of Association Rules for Horizontally Distributed Databases Based on FP-Tree

The discovery of frequent patterns, association rules, and correlation relationships among huge amounts of data is useful to business intelligence in this big data era. We propose a new scheme which is a secure and efficient association rule mining (ARM) method on horizontally partitioned databases. We enhance the performance of ARM on distributed databases by combining Apriori algorithm and FP-tree in this new situation. To help the implement of combining Apriori algorithm and FP-tree on distributed databases, we originally come up with a method of merging FP-tree in our scheme. We take advantage of Homomorphic Encryption to guarantee the security and efficiency of data operation in our scheme. More speficially, we use Paillier’s homomorphic encryption method which only has addition homogeneity to encrypt items’ supports. At last, we perform experimental analysis for our scheme to show that our proposal outperform the existing schemes.

Detect SIP Flooding Attacks in VoLTE by Utilizing and Compressing Counting Bloom Filter

As a new generation voice service, Voice over LTE (VoLTE) has attracted worldwide attentions in both the academia and industry. Different from the traditional voice call based on circuit-switched (CS), VoLTE evolves into the packet-switched (PS) field, which is quite open to the public. Though designed rigorously, similar to VoIP service, VoLTE also suffers from SIP (Session Initiation Protocal) flooding attacks. In this paper, two schemes inspired by Counting Bloom Filter (CBF) are proposed to thwart these attacks. In scheme I, we leverage CBF to accomplish flooding attack detection. In scheme II, we design a versatile CBF-like structure, PFilter, to achieve the same goal. Compared with previous relevant works, our detection schemes gain advantages in many aspects including low-rate flooding attack and stealthy flooding attack. Moreover, not only can our schemes detect the attacks with high accuracy, but also find out the attacker to ensure normal operation of VoLTE. Extensive experiments are performed to well evaluate the performance of the proposed two schemes.

Privacy-Preserving Fraud Detection via Cooperative Mobile Carriers with Improved Accuracy

With the explosive growth of users in mobile carrier, telecommunication fraud causes a serious loss to both of the users and carriers. The academia has an increasing interest in the issue of detecting and recognizing fraudster, and varies strategies have been proposed to prevent the attack and fraudulent activity. However, fraudsters are always inclined to hide their identity and perform the fraudulent activity through different mobile carriers, which makes the previous methods less effective in fraud detection. In this paper, we propose a novel strategy with a high accuracy and security through the cooperation among mobile carriers. We introduce the Latent Dirichlet Allocation (LDA) model to profile users in different carriers. In order to match the fraud accounts, we propose a strategy based on Maximum Mean Discrepancy (MMD) to analyze and compare the distribution of statistical samples. Meantime, during the cooperation of carriers, there is a risk of privacy disclosure. To deal with this weakness, we also demonstrate that our method can detect the fraudulent accounts without leaking the private records and data of user accounts based on the differential privacy.