Machine learning algorithms are increasingly used to assist human decision-making. When the goal of machine assistance is to improve the accuracy of human decisions, it might seem appealing to design ML algorithms that complement human knowledge. While neither the algorithm nor the human are perfectly accurate, one could expect that their complementary expertise might lead to improved outcomes. In this study, we demonstrate that in practice decision aids that are not complementary, but make errors similar to human ones may have their own benefits. In a series of human-subject experiments with a total of 901 participants, we study how the similarity of human and machine errors influences human perceptions of and interactions with algorithmic decision aids. We find that (i) people perceive more similar decision aids as more useful, accurate, and predictable, and that (ii) people are more likely to take opposing advice from more similar decision aids, while (iii) decision aids that are less similar to humans have more opportunities to provide opposing advice, resulting in a higher influence on people’s decisions overall.

Federated Learning allows distributed entities to train a common model collaboratively without sharing their own data. Although it prevents data collection and aggregation by exchanging only parameter updates, it remains vulnerable to various inference and reconstruction attacks where a malicious entity can learn private information about the participants' training data from the captured gradients. Differential Privacy is used to obtain theoretically sound privacy guarantees against such inference attacks by noising the exchanged update vectors. However, the added noise is proportional to the model size which can be very large with modern neural networks. This can result in poor model quality. In this paper, compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy. We show experimentally, using 2 datasets, that our privacy-preserving proposal can reduce the communication costs by up to 95 % with only a negligible performance penalty compared to traditional non-private federated learning schemes.

Federated learning becomes a prominent approach when different entities want to learn collaboratively a common model without sharing their training data. %Compared to traditional machine learning, it does not require to collect and centralize all data before training a common model. However, Federated learning has two main drawbacks. First, it is quite bandwidth inefficient as it involves a lot of message exchanges between the aggregating server and the participating entities. This bandwidth and corresponding processing costs could be prohibitive if the participating entities are, for example, mobile devices. Furthermore, although federated learning improves privacy by not sharing data, recent attacks have shown that it still leaks information about the training data. This paper presents a novel privacy-preserving federated learning scheme. The proposed scheme provides theoretical privacy guarantees, as it is based on Differential Privacy. Furthermore, it optimizes the model accuracy by constraining the model learning phase on few selected weights. Finally, as shown experimentally, it reduces the upstream and downstream bandwidth by up to 99.9% compared to standard federated learning, making it practical for mobile systems.

The intrusiveness of Web tracking and the increasing invasiveness of digital advertising have raised serious concerns regarding user privacy and Web usability, leading a substantial chunk of the populace to adopt ad-blocking technologies in recent years. The problem with these technologies, however, is that they are extremely limited and radical in their approach, and they completely disregard the underlying economic model of the Web, in which users get content free in return for allowing advertisers to show them ads. Nowadays, with around 200 million people regularly using such tools, said economic model is in danger. In this article, we investigate an Internet technology that targets users who are not, in general, against advertising, accept the trade-off that comes with the “free” content, but—for privacy concerns—they wish to exert fine-grained control over tracking. Our working assumption is that some categories of web pages (e.g., related to health or religion) are more privacy-sensitive to users than others (e.g., about education or science). Capitalizing on this, we propose a technology that allows users to specify the categories of web pages that are privacy-sensitive to them and block the trackers present on such web pages only. As tracking is prevented by blocking network connections of third-party domains, we avoid not only tracking but also third-party ads. Since users continue receiving ads on those web pages that belong to non-sensitive categories, our approach may provide a better point of operation within the trade-off between user privacy and the Web economy. To test the appropriateness and feasibility of our solution, we implemented it as a Web-browser plug-in, which is currently available for Google Chrome and Mozilla Firefox. Experimental results from the collected data of 746 users during one year show that only 16.25% of ads are blocked by our tool, which seems to indicate that the economic impact of the ad-blocking exerted by privacy-sensitive users could be significantly reduced.

Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models. We model the generator distribution of the training data by a mixture of k generative neural networks. These are trained together and collectively learn the generator distribution of a dataset. Data is divided into k clusters, using a novel differentially private kernel k-means, then each cluster is given to separate generative neural networks, such as Restricted Boltzmann Machines or Variational Autoencoders, which are trained only on their own cluster using differentially private gradient descent. We evaluate our approach using the MNIST dataset and a large Call Detail Records dataset, showing that it produces realistic synthetic samples, which can also be used to accurately compute arbitrary number of counting queries.

The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and antitracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web—in which users get content free in return for allowing advertisers to show them ads—is at serious peril. In this article, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.

Abstract Several recent studies have demonstrated that people show large behavioural uniqueness. This has serious privacy implications as most individuals become increasingly re-identifiable in large datasets or can be tracked, while they are browsing the web, using only a couple of their attributes, called as their fingerprints. Often, the success of these attacks depends on explicit constraints on the number of attributes learnable about individuals, i.e., the size of their fingerprints. These constraints can be budget as well as technical constraints imposed by the data holder. For instance, Apple restricts the number of applications that can be called by another application on iOS in order to mitigate the potential privacy threats of leaking the list of installed applications on a device. In this work, we address the problem of identifying the attributes (e.g., smartphone applications) that can serve as a fingerprint of users given constraints on the size of the fingerprint. We give the best fingerprinting algorithms in general, and evaluate their effectiveness on several real-world datasets. Our results show that current privacy guards limiting the number of attributes that can be queried about individuals is insufficient to mitigate their potential privacy risks in many practical cases.

Set-valued dataset contains different types of items/values per individual, for example, visited locations, purchased goods, watched movies, or search queries. As it is relatively easy to re-identify individuals in such datasets, their release poses significant privacy threats. Hence, organizations aiming to share such datasets must adhere to personal data regulations. In order to get rid of these regulations and also to beneit from sharing, these datasets should be anonymized before their release. In this paper, we revisit the problem of anonymizing set-valued data. We argue that anonymization techniques targeting traditional km-anonymity model, which limits the adversarial background knowledge to at most m items per individual, are impractical for large real-world datasets. Hence, we propose a probabilistic relaxation of km-anonymity and present an anonymization technique to achieve it. This relaxation also improves the utility of the anonymized data. We also demonstrate the effectiveness of our scalable anonymization technique on a real-world location dataset consisting of more than 4 million subscribers of a large European telecom operator. We believe that our technique can be very appealing for practitioners willing to share such large datasets.

With billions of handsets in use worldwide, the quantity of mobility data is gigantic. When aggregated they can help understand complex processes, such as the spread viruses, and built better transportation systems, prevent traffic congestion. While the benefits provided by these datasets are indisputable, they unfortunately pose a considerable threat to location privacy.

We investigate the potential to use browsing habits and browser history as a new authentication and identification system for the Web with potential applications to anomaly and fraud detection. For the first time, we provide an empirical analysis using data from $4,578$ users. We employ the traditional biometric analysis and show that the False Acceptance Rate can be low ($FAR=1.1%$), though this results in a relatively high False Rejection Rate ($FRR=13.8%$). The scheme may either be utilized by Web service providers (with access to user's browser history) or any Webmaster, using other specialized techniques such as timing-based browser cache sniffing or a browser extension. We construct such a proof-of-concept extension.

Sequential data is being increasingly used in a variety of applications. Publishing sequential data is of vital importance to the advancement of these applications. However, as shown by the re-identification attacks on the AOL and Netflix datasets, releasing sequential data may pose considerable threats to individual privacy. Recent research has indicated the failure of existing sanitization techniques to provide claimed privacy guarantees. It is therefore urgent to respond to this failure by developing new schemes with provable privacy guarantees. Differential privacy is one of the only models that can be used to provide such guarantees. Due to the inherent sequentiality and high-dimensionality, it is challenging to apply differential privacy to sequential data. In this paper, we address this challenge by employing a variable-length n-gram model, which extracts the essential information of a sequential database in terms of a set of variable-length n-grams. Our approach makes use of a carefully designed exploration tree structure and a set of novel techniques based on the Markov assumption in order to lower the magnitude of added noise. The published n-grams are useful for many purposes. Furthermore, we develop a solution for generating a synthetic database, which enables a wider spectrum of data analysis tasks. Extensive experiments on real-life datasets demonstrate that our approach substantially outperforms the state-of-the-art techniques.

Differential privacy has emerged as one of the most promising privacy models for private data release. It can be used to release different types of data, and, in particular, histograms, which provide useful summaries of a dataset. Several differentially private histogram releasing schemes have been proposed recently. However, most of them directly add noise to the histogram counts, resulting in undesirable accuracy. In this paper, we propose two sanitization techniques that exploit the inherent redundancy of real-life datasets in order to boost the accuracy of histograms. They lossily compress the data and sanitize the compressed data. Our first scheme is an optimization of the Fourier Perturbation Algorithm (FPA) presented in [13]. It improves the accuracy of the initial FPA by a factor of 10. The other scheme relies on clustering and exploits the redundancy between bins. Our extensive experimental evaluation over various real-life and synthetic datasets demonstrates that our techniques preserve very accurate distributions and considerably improve the accuracy of range queries over attributed histograms.

Generating secret keys in mobile wireless networks is considered a challenging problem where a key management infrastructure is not always available. Recent security methods have shown that secret keys can be generated using Ultra Wide Band (UWB) channels. These solutions rely on relevant channel properties such as reciprocity and spatial decorrelation. Accordingly, the radio channel responses can be used as common information to derive secret keys shared by legitimate parties. However, novel studies in the field of UWB channel prediction have demonstrated that channel profiles could be reliably inferred using for instance Ray-Tracing tools. This paper explores this technique to perform attacks and to evaluate the security of UWB secret key generation methods. The main observation here is that it is difficult for a third party to obtain the exact channel responses; thus to retrieve the secret keys. The robustness of UWB key generation methods then depends on the complexity for attackers to describe precisely the physical environment and on the post processing methods to agree on the same key (i.e., quantization, erroneous bits detection, etc.).

The increasing amount of personal and sensitive information disseminated over the Internet prompts commen-surately growing privacy concerns. Digital data often lingers indefinitely and users lose its control. This motivates the desire to restrict content availability to an expiration time set by the data owner. This paper presents and formalizes the notion of Ephemeral Publishing (EphPub), to prevent the access to expired content. We propose an efficient and robust protocol that builds on the Domain Name System (DNS) and its caching mechanism. With EphPub, sensitive content is published encrypted and the key material is distributed, in a steganographic manner, to randomly selected and independent resolvers. The availability of content is then limited by the evanescence of DNS cache entries. The EphPub protocol is transparent to existing applications, and does not rely on trusted hardware, centralized servers, or user proactive actions. We analyze its robustness and show that it incurs a negligible overhead on the DNS infrastructure. We also perform a large-scale study of the caching behavior of 900K open DNS resolvers. Finally, we propose Firefox and Thunderbird extensions that provide ephemeral publishing capabilities, as well as a command-line tool to create ephemeral files.

A formal treatment to the security of Concealed Data Aggregation (CDA) and the more general Private Data Aggregation (PDA) is given. While there exist a handful of constructions, rigorous security models and analyses for CDA or PDA are still lacking. Standard security notions for public key encryption, including semantic security and indistinguishability against chosen ciphertext attacks, are refined to cover the multisender nature and aggregation functionality of CDA and PDA in the security model. The proposed security model is sufficiently general to cover most application scenarios and constructions of privacy-preserving data aggregation. An impossibility result on achieving security against adaptive chosen ciphertext attacks in CDA/PDA is shown. A generic CDA construction based on public key homomorphic encryption is given, along with a proof of its security in the proposed model. The security of a number of existing schemes is analyzed in the proposed model.

Recent security methods propose to generate secret keys from Ultra Wide Band (UWB) channels. These solutions rely on the reciprocity and spatial channel correlation principles. This work aims at presenting empirical studies on the aforesaid properties. First, we verify the UWB reciprocity for different multipath scenarios. In these experiences, the reciprocity is always valid independently of distance between the receiver and emitter. However, we show that using an asymmetric hardware in up and down links, can affect the channel similarity. Secondly, we report measurements of spatial correlation in near and far field channel. Various experimental scenarios are tested to validate location channel variations. We observe that in very close and far receiver's locations, there is no correlation. This variation is not depending on distance but mainly on the indoor environment. In addition, various channel parameters: channel impulse response, channel envelope, and power delay profile have been investigated. We show that channel properties rely on these parameters.

We present an overview of end-to-end encryption solutions for convergecast traffic in wireless sensor networks that support in-network processing at forwarding intermediate nodes. Other than hop-by-hop based encryption approaches, aggregator nodes can perform in-network processing on encrypted data. Since it is not required to decrypt the incoming ciphers before aggregating, substantial advantages are 1) neither keys nor plaintext is available at aggregating nodes, 2) the overall energy consumption of the backbone can be reduced, 3) the system is more flexible with respect to changing routes, and finally 4) the overall system security increases. We provide a qualitative comparison of available approaches, point out their strengths, respectively weaknesses, and investigate opportunities for further research.

Device attestation is an essential feature in many security protocols and applications. The lack of dedicated hardware and the impossibility to physically access devices to be attested, makes attestation of embedded devices, in applications such as Wireless Sensor Networks, a prominent challenge. Several software-based attestation techniques have been proposed that either rely on tight time constraints or on the lack of free space to store malicious code. This paper investigates the shortcomings of existing software-based attestation techniques. We first present two generic attacks, one based on a return-oriented rootkit} and the other on code compression. We further describe specific attacks on two existing proposals, namely SWATT and ICE-based schemes, and argue about the difficulty of fixing them. All attacks presented in this paper were implemented and validated on commodity sensors.

We propose a proximity-based access control scheme for implantable medical devices (IMDs). Our scheme is based on ultrasonic distance-bounding and enables an implanted medical device to grant access to its resources only to those devices that are in its close proximity. We demonstrate the feasibility of our approach through tests in an emulated patient environment. We show that, although implanted, IMDs can successfully verify the proximity of other devices with high accuracy. We propose a set of protocols that support our scheme, analyze their security in detail and discuss possible extensions. We make new observations about the security of implementations of ultrasonic distance-bounding protocols. Finally, we discuss the integration of our scheme with existing IMD devices and with their existing security measures.

Fast-flux is a redirection technique used by cyber-criminals to hide the actual location of malicious servers. Its purpose is to evade identification and prevent or, at least delay, the shutdown of these illegal servers by law enforcement.

Wireless sensor networks (WSNs) are composed of tiny devices with limited computation and battery capacities. For such resource-constrained devices, data transmission is a very energy-consuming operation. To maximize WSN lifetime, it is essential to minimize the number of bits sent and received by each device. One natural approach is to aggregate sensor data along the path from sensors to the sink. Aggregation is especially challenging if end-to-end privacy between sensors and the sink (or aggregate integrity) is required. In this article, we propose a simple and provably secure encryption scheme that allows efficient additive aggregation of encrypted data. Only one modular addition is necessary for ciphertext aggregation. The security of the scheme is based on the indistinguishability property of a pseudorandom function (PRF), a standard cryptographic primitive. We show that aggregation based on this scheme can be used to efficiently compute statistical values, such as mean, variance, and standard deviation of sensed data, while achieving significant bandwidth savings. To protect the integrity of the aggregated data, we construct an end-to-end aggregate authentication scheme that is secure against outsider-only attacks, also based on the indistinguishability property of PRFs.

Harvard architecture CPU design is common in the embedded world. Examples of Harvard-based architecture devices are the Mica family of wireless sensors. Mica motes have limited memory and can process only very small packets. Stack-based buffer overflow techniques that inject code into the stack and then execute it are therefore not applicable. It has been a common belief that code injection is impossible on Harvard architectures. This paper presents a remote code injection attack for Mica sensors. We show how to exploit program vulnerabilities to permanently inject any piece of code into the program memory of an Atmel AVR-based sensor. To our knowledge, this is the first result that presents a code injection technique for such devices. Previous work only succeeded in injecting data or performing transient attacks. Injecting permanent code is more powerful since the attacker can gain full control of the target sensor. We also show that this attack can be used to inject a worm that can propagate through the wireless sensor network and possibly create a sensor botnet. Our attack combines different techniques such as return oriented programming and fake stack injection. We present implementation details and suggest some counter-measures.

In data aggregation, multiple source nodes send their data to a sink along a concast tree with aggregation done en route so that the sink can obtain the aggregate (which could be the sum, average, etc.) of all these data. End-to-end privacy and aggregate integrity are the two main goals of secure data aggregation. While the privacy goal has been widely studied, providing end-to-end aggregate integrity in the presence of possibly compromised aggregating nodes remains largely an open problem. Message Authentication Codes (MAC) are commonly used to provide end-to-end data integrity in two party settings. Natural extensions of MAC for the data aggregation scenario are considered. It is shown that a straightforward and intuitive refinement of the MAC security model (for the data aggregation setting) is not achievable. A weaker security notion is proposed; whether this notion is achievable remains unclear.

We present a new encryption mode of operation that allows nodes of a network to exchange messages securely (i.e. encrypted and authenticated) without sharing a common key or using public key cryptography. Our scheme is well adapted to networks, such as ad hoc, overlay or sensor networks, where nodes have limited capabilities and can share only a small number of symmetric keys. It provides privacy and integrity protection. We show that our proposal can be used in wireless sensor networks to send encrypted packets to very dynamic sets of nodes without having to establish and maintain group keys. These sets of nodes can be explicitly specified by the source or can be specified by the network according to some criteria, such as their location, proximity to an object, temperature range. As a result, a node can, for example, send encrypted data to all the nodes within a given geographical area, without having to identify the destination nodes in advance. Finally we show that our proposal can be used to implement a secure and scalable aggregation scheme for wireless sensor networks.

Much of today's distributed computing takes place in a client /server model. Despite advances in fault tolerance - in particular, replication and load distribution -- server overload remains to be a major problem. In the Web context, one of the main overload factors is the direct consequence of expensive Public Key operations performed by servers as part of each SSL handshake. Since most SSL-enabled servers use RSA, the burden of performing many costly decryption operations can be very detrimental to server performance. This paper examines a promising technique for re-balancing RSA-based client/server handshakes. This technique facilitates more favorable load distribution by requiring clients to perform more work (as part of encryption) and servers to perform commensurately less work, thus resulting in better SSL throughput. Proposed techniques are based on careful adaptation of variants of Server-Aided RSA originally constructed by Matsumoto, et al. [1]. Experimental results demonstrate that suggested methods (termed Client-Aided RSA) can speed up processing of RSA private key operations by a factor of between 11 to 19, depending on the RSA key size. This represents a considerable improvement. Furthermore, proposed techniques can be a useful companion tool for SSL Client Puzzles in defense against DoS and DDoS attacks.

We present a stateless defense against the neighbor discovery denial-of-service (ND-DoS) attack in IPv6. The ND-DoS attack consists of remotely flooding a target subnet with bogus packets destined for random interface identifiers; a different one for each malicious packet. The 128-bit IPv6 address reserves its 64 low-order bits for the interface ID. Consequently, the malicious packets are very likely to fall on previously unresolved addresses and the target access router (or leaf router) is obligated to resolve these addresses by sending neighbor solicitation packets. Neighbor solicitation packets are link layer multicast (or broadcast), and hence also forwarded by bridges. As a consequence, the attack may consume important bandwidth in subnets with wireless bridges, or access points. This problem is particularly important in the presence of mobile IPv6 devices that expect incoming sessions from the Internet. In this case, address resolution is crucial for the access router to reliably deliver incoming sessions to idle mobile devices with unknown MAC addresses. We propose a novel neighbor solicitation technique using Bloom filters. Multiple IPv6 addresses (bogus or real) that are waiting in the access router's address resolution queue are compactly represented using a Bloom filter. By broadcasting a single neighbor solicitation message that carries the Bloom filter, multiple IPv6 addresses are concurrently solicited. Legitimate neighbor solicitation triggering packets are not denied service. An on-link host can detect its address in the received Bloom filter and return its MAC address to the access router. A bandwidth gain around 40 can be achieved in all cells of the target subnet. This approach that we call compact neighbor discovery (CND) is the first bandwidth DoS defense that we are aware of to employ a bandwidth optimization.

Wireless sensor networks (WSNs) are ad-hoc networks composed of tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to the lifetime of a WSN to minimize the number of bits sent by each device. One well-known approach is to aggregate sensor data (e.g., by adding) along the path from sensors to the sink. Aggregation becomes especially challenging if end-to-end privacy between sensors and the sink is required. In this paper, we propose a simple and provably secure additively homomorphic stream cipher that allows efficient aggregation of encrypted data. The new cipher only uses modular additions (with very small moduli) and is therefore very well suited for CPU-constrained devices. We show that aggregation based on this cipher can be used to efficiently compute statistical values such as mean, variance and standard deviation of sensed data, while achieving significant bandwidth gain.

This paper presents a new pairing protocol that allows two CPU-constrained wireless devices Alice and Bob to establish a shared secret at a very low cost. To our knowledge, this is the first software pairing scheme that does not rely on expensive public-key cryptography, out-of-band channels (such as a keyboard or a display) or specific hardware, making it inexpensive and suitable for CPU-constrained devices such as sensors.

Secret handshake protocols were recently introduced by Balfanz, et al. [1] to allow members of the same group to authenticate each other secretly, in the sense that someone who is not a group member cannot tell, by engaging in the handshake protocol, whether his counterparty is a member of the group. On the other hand, any two parties who are members of the same group will recognize each other as members. Thus, secret handshakes can be used in any scenario where group members need to identify each other without revealing their group affiliations to outsiders. The secret handshake protocol of [1] relies on a Bilinear Diffie-Hellman assumption on certain elliptic curves. We show how to build secret handshake protocols secure under more standard cryptographic assumptions, like the RSA or the Diffie Hellman (DH) assumption, using a novel tool of CA-oblivious public key encryption, i.e. an encryption scheme where neither the public key nor the ciphertext reveal any information about the Certification Authority which certified the public key.

This paper addresses the identifier ownership problem. It does so by using characteristics of Statistical Uniqueness and Cryptographic Verifiability (SUCV) of certain entities which this document calls SUCV Identifiers and Addresses, or, alternatively, Crypto-based Identifiers. Their characteristics allow them to severely limit certain classes of denial-of-service attacks and hijacking attacks. SUCV addresses are particularly applicable to solve the address ownership problem that hinders mechanisms like Binding Updates in Mobile IPv6.

Concurrently, group membership management in IP multicast and anycast can be abused in order to launch denial-of-service (DoS) attacks. The root of the problem is that routers cannot determine if a given host is authorized to join group. We propose a solution for Ipv6-based on group cryptographically generated addresses (G-CGA). These addresses have characteristics of statistical uniqueness and cryptographic verifiability that lend themselves to severely limiting certain classes of DoS attacks. Our scheme is fully distributed and does not require any trusted third party or pre-established security association between the routers and the hosts. This is not only a huge gain in terms of scalability, reliability and overhead, but also in terms of privacy.

The IETF is currently working on service differentiation in the Internet. However, in wireless environments where bandwidth is scarce and channel conditions are variable, IP differentiated services are sub-optimal without lower layers' support. We present three service differentiation schemes for IEEE 802.11. The first one is based on scaling the contention window according to the priority of each flow or user. The second one assigns different inter-frame spacings to different users. Finally, the last one uses different maximum frame lengths for different users. We simulate and analyze the performance of each scheme with TCP and UDP flows.

The IETF is currently working on service differentiation for the Internet. However service differentiation at the IP layer is useless without support of lower layers. This support is even more critical in wireless environments because of the dynamism of the channel conditions and of the network topology. We present a service differentiation support for the IEEE 802.11. The idea is to scale the contention window according to the priority of each flow or user. Preliminary simulation results are shown when using this mechanism with TCP and UDP.

This paper proposes to extend mobile IP with an adaptive individual paging scheme. Paging reduces the signaling cost of mobile IP making it more adapted to wireless cellular IP networks. By reducing the number of location updates to be sent per mobile host, paging also minimizes power consumption of the mobile devices. In the proposed extension, each mobile host computes dynamically its optimal location area size according to its traffic and mobility parameters. The optimal size is the size that provides the best paging versus location update cost tradeoff and, as a result, minimizes the signaling load in the network. We show that our extension provides a significant gain compared to mobile IP.

As the number of portable devices roaming across the Internet increases, the problem of routing packets to mobile hosts generates increasing research and commercial interest. This paper presents a hierarchical scheme to support mobility in the Internet. This scheme is based on the multicast routing protocol PIM-SM. In this proposal, a mobile node is seen by its correspondent nodes as a special multicast group. Packets destined for a mobile host are delivered to its RP (rendezvous point) and then forwarded to the mobile node's current location. Our scheme has two main benefits. First, it is hierarchical and therefore reduces handoff latency and the load on the Internet. Second, it uses IP multicast and consequently benefits from its diffusion property, which can be very useful for providing smooth subnet handoffs. We compare the performance of our approach with the IETF Mobile IPv6 proposal. We show that while our approach has a larger memory space requirement, it reduces the network load, provides smoother and faster subnet handoffs and frees the mobile hosts from mobility support procedures.

Fueled by the large number of powerful light-weight portable computers, the expanding availability of wireless networks, and the popularity of the Internet, there is an increasing demand to connect portable computers to the Internet at any time and in any place. However, the dynamic nature of such connectivity requires more flexible network support than has typically been available for stationary workstations. This paper introduces the following two mechanisms, in the context of Mobile IP [24], to ensure a mobile host's convenient and efficient communication with other hosts in a changing environment. One mechanism supports multiple packet delivery methods (such as regular IP or Mobile IP) and adaptively selects the most appropriate one to use according to the characteristics of each traffic flow. The other mechanism enables a mobile host to make use of multiple active network interfaces simultaneously and to control the selection of the most desirable network interfaces for both outgo...

A protocol compiler takes as input an abstract specification of a protocol and generates an implementation of that protocol. Protocol compilers usually produce inefficient code both in terms of code speed and code size. We show that the combination of two techniques makes it possible to build protocol compilers that generate efficient code. These techniques are: (i) the use of a compiler that generates from the specification a unique tree-shaped automation (rather than multiple independent automata) and (ii) the use of optimization techniques applied at the automation level, i.e., on the branches of the trees. We have developed a protocol compiler that uses both these techniques. The compiler takes as the input a protocol specification written in the synchronous language Esterel. The specification is compiled into a unique automation by the Esterel front end compiler. The automation is then optimized and converted into C code by our protocol optimizer called HIPPCO. HIPPCO improves the code performance and reduces the code size by simultaneously optimizing the performance of the common path and optimizing the size of the uncommon path. We evaluate the gain expected with our approach on a real-life example, namely a working subset of the TCP protocol generated from an Esterel specification. We compare the protocol code generated with our approach to that derived from the standard BSD TCP implementation. The results are very encouraging. HIPPCO-generated code executes up to 25% fewer instructions than the BSD code for input packet processing while only increasing the code size by 25%.

A protocol compiler takes as input an abstract specification of a protocol and generates an implementation of that protocol. Protocol compilers usually produce inefficient code both in terms of code speed and code size. In this paper, we show that the combination of two techniques makes it possible to build protocol compilers that generate efficient code. These techniques are i) the use of a compiler that generates from the specification a unique tree-shaped automaton (rather than multiple independent automata), and ii) the use of optimization techniques applied at the automaton level, i.e. on the branches of the trees.We have developed a protocol compiler that uses both these techniques. The compiler takes as input a protocol specification written in the synchronous language Esterel. The specification is compiled into a unique automaton by the Esterel front end compiler. The automaton is then optimized and converted into C code by our protocol optimizer called HIPPCO. HIPPCO improves code performance and reduces code size by simultaneously optimizing the performance of the common path and optimizing the size of the uncommon path. We evaluate the gain expected with our approach on a real-life example, namely a working subset of the TCP protocol generated from an Esterel specification. We compare the protocol code generated with our approach to that derived from the standard BSD TCP implementation. The results are very encouraging. HIPPCO-generated code executes up to 25 % fewer instructions than the BSD code for input packet processing while maintaining comparable code size.

A waveform substitution technique using interpolation based on the slowly varying speech parameters of short-time energy and zero-crossing information is developed for a packetized speech communication system. The system uses 64-kb conventional pulse code modulation (PCM) for encoding and takes advantage of active talkspurts and silence intervals to increase the efficiency of utilizing a digital link. The short-time energy and information on the zero-crossings needed for the purpose of determining talkspurts are transmitted in a preceding packet. Hence, when a packet is pronounced lost, its envelope and frequency characteristics are obtained from a previous packet and used to synthesize a substitution waveform which is free of annoying sounds that are due to abrupt changes in amplitude.>