Protecting User Privacy in a Multi-Path Information-Centric Network Using Multiple Random-Caches

In-network caching is a fundamental mechanism advocated by information-centric networks （ICNs） for efficient content delivery. However, this new mechanism also brings serious privacy risks due to cache snooping attacks. One effective solution to this problem is random-cache, where the cache in a router randomly mimics a cache hit or a cache miss for each content request/probe. In this paper, we investigate the effectiveness of using multiple random-caches to protect cache privacy in a multi-path ICN. We propose models for characterizing the privacy of multi-path ICNs with random-caches, and analyze two different attack scenarios： 1） prefix-based attacks and 2） suffix-based attacks. Both homogeneous and heterogeneous caches are considered. Our analysis shows that in a multi-path ICN an adversary can potentially gain more privacy information by adopting prefix-based attacks. Furthermore, heterogeneous caches provide much better privacy protection than homogeneous ones under both attacks. The effect of different parameters on the privacy of multi-path random-caches is further investigated, and the comparison with its single-path counterpart is carried out based on numerical evaluations. The analysis and results in this paper provide insights in designing and evaluating multi-path ICNs when we take privacy into consideration.

Enhancing Security by Using GIFT and ECC Encryption Method in Multi-Tenant Datacenters

Data security and user privacy have become crucial elements in multi-tenant data centers.Various traffic types in the multi-tenant data center in the cloud environment have their characteristics and requirements.In the data center network(DCN),short and long flows are sensitive to low latency and high throughput,respectively.The traditional security processing approaches,however,neglect these characteristics and requirements.This paper proposes a fine-grained security enhancement mechanism(SEM)to solve the problem of heterogeneous traffic and reduce the traffic completion time(FCT)of short flows while ensuring the security of multi-tenant traffic transmission.Specifically,for short flows in DCN,the lightweight GIFT encryption method is utilized.For Intra-DCN long flows and Inter-DCN traffic,the asymmetric elliptic curve encryption algorithm(ECC)is utilized.The NS-3 simulation results demonstrate that SEM dramatically reduces the FCT of short flows by 70%compared to several conventional encryption techniques,effectively enhancing the security and anti-attack of traffic transmission between DCNs in cloud computing environments.Additionally,SEM performs better than other encryption methods under high load and in largescale cloud environments.

The Role of Consumer Privacy Concerns in Shaping Platform Strategy for Online Markets

We investigate the effects of consumer privacy concerns on the pricing and personal data collection strategy of an online platform.The online platform derives revenues from disclosing consumer information to firms.Firms compete for the information in order to enable them to price discriminate and thus derive revenues from consumer purchases.A novel aspect of our research is that we allow the online platform to sell only a subset of consumer data.We develop analytical models where consumers can/cannot protect their privacy.Our analysis yields three main conclusions.First,in the monopoly case,we find that when the consumer privacy disclosure aversion cost is relatively low,it is optimal for the platform to sell all consumer information to the firm.Second,in the duopoly case,we illustrate that when the consumer privacy disclosure aversion cost is relatively low,the platform will sell all consumer information to only one firm;when the cost is moderate,the platform will choose to sell the information of only some consumers and to only one firm;when the cost is relatively high,the platform will select only some of the consumers and sell their information to both firms.Third,it will be better for the platform to provide the information protection service for free when the privacy cost is low.

HilAnchor:Location Privacy Protection in the Presence of Users' Preferences

Location privacy receives considerable attentions in emerging location based services.Most current practices however either ignore users＇ preferences or incompletely fulfill privacy preferences.In this paper,we propose a privacy protection solution to allow users＇ preferences in the fundamental query of k nearest neighbors （kNN）.Particularly,users are permitted to choose privacy preferences by specifying minimum inferred region.Via Hilbert curve based transformation,the additional workload from users＇ preferences is alleviated.Furthermore,this transformation reduces time-expensive region queries in 2-D space to range the ones in 1-D space.Therefore,the time efficiency,as well as communication efficiency,is greatly improved due to clustering properties of Hilbert curve.Further,details of choosing anchor points are theoretically elaborated.The empirical studies demonstrate that our implementation delivers both flexibility for users＇ preferences and scalability for time and communication costs.

Privacy-Aware Examination Results Ranking for the Balance Between Teachers and Mothers

As the main parent and guardian, mothers are often concerned with the study performance of their children.More specifically, most mothers are eager to know the concrete examination scores of their children. However,with the continuous progress of modern education systems, most schools or teachers have now been forbidden to release sensitive student examination scores to the public due to privacy concerns, which has made it infeasible for mothers to know the real study level or examination performance of their children. Therefore, a conflict has come to exist between teachers and mothers, which harms the general growing up of students in their study. In view of this challenge, we propose a Privacy-aware Examination Results Ranking(PERR) method to attempt at balancing teachers’ privacy disclosure concerns and the mothers’ concerns over their children’s examination performance.By drawing on a relevant case study, we prove the effectiveness of the proposed PERR method in evaluating and ranking students according to their examination scores while at the same time securing sensitive student information.

Using IM-Visor to stop untrusted IME apps from stealing sensitive keystrokes

Third-party IME(Input Method Editor)apps are often the preference means of interaction for Android users’input.In this paper,we first discuss the insecurity of IME apps,including the Potentially Harmful Apps(PHAs)and malicious IME apps,which may leak users’sensitive keystrokes.The current defense system,such as I-BOX,is vulnerable to the prefix substitution attack and the colluding attack due to the post-IME nature.We provide a deeper understanding that all the designs with the post-IME nature are subject to the prefix-substitution and colluding attacks.To remedy the above post-IME system’s flaws,we propose a new idea,pre-IME,which guarantees that“Is this touch event a sensitive keystroke?”analysis will always access user touch events prior to the execution of any IME app code.We design an innovative TrustZone-based framework named IM-Visor which has the pre-IME nature.Specifically,IM-Visor creates the isolation environment named STIE as soon as a user intends to type on a soft keyboard,then the STIE intercepts,Android event sub translates and analyzes the user’s touch input.If the input is sensitive,the translation of keystrokes will be delivered to user apps through a trusted path.Otherwise,IM-Visor replays non-sensitive keystroke touch events for IME apps or replays non-keystroke touch events for other apps.A prototype of IM-Visor has been implemented and tested with several most popular IMEs.The experimental results show that IM-Visor has small runtime overheads.