Functional Encryption (FE) allows users to extract specific function-related information from encrypted data while preserving the privacy of the underlying plaintext. Though significant research has been devoted to developing secure and efficient Multi-Input Functional Encryption schemes supporting diverse functions, there remains a noticeable research gap in the development of verifiable FE schemes. Functionality and performance have received considerable attention, however, the crucial aspect of verifiability in FE has been relatively understudied. Another important aspect that prior research in FE with outsourced decryption has not adequately addressed is the fairness of the data-for-money exchange between a curator and an analyst. This paper focuses on addressing these gaps by proposing a verifiable FE scheme for inner product computation. The scheme not only supports the multi-client setting but also extends its functionality to accommodate multiple users -- an essential feature in modern privacy-respecting services. Additionally, it demonstrates how this FE scheme can be effectively utilized to ensure fairness and atomicity in a payment protocol, further enhancing the trustworthiness of data exchanges.

We propose a novel obfuscation technique that can be used to outsource hard satisfiability (SAT) formulas to the cloud. Servers with large computational power are typically used to solve SAT instances that model real-life problems in task scheduling, AI planning, circuit verification and more. However, outsourcing data to the cloud may lead to privacy and information breaches since satisfying assignments may reveal considerable information about the underlying problem modeled by SAT.In this work, we develop CENSOR, a SAT obfuscation framework that resembles Indistinguishability Obfuscation. At the core of the framework lies a mechanism that transforms any formula to a random one with the same number of satisfying assignments. As a result, obfuscated formulas are indistinguishable from each other thus preserving the input-output privacy of the original SAT instance. Contrary to prior solutions that are rather adhoc in nature, we formally prove the security of our scheme. Additionally, we show that obfuscated formulas are within a polynomial factor of the original ones thus achieving polynomial slowdown. Finally, the whole process is efficient in practice, allowing solutions to original instances to be easily recovered from obfuscated ones. A byproduct of our method is that all NP problems can be potentially outsourced to the cloud by means of reducing to SAT.

The use of data combined with tailored statistical analysis has presented a unique opportunity to organizations in diverse fields to observe users' behaviors and needs, and accordingly adapt and fine-tune their services. However, in order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. The use of statistical databases for releasing data analytics is growing exponentially, and while many cryptographic methods are utilized to protect the confidentiality of the data -- a task that has been ably carried out by many authors over the years -- only a few %rudimentary number of works focus on the problem of privatizing the actual databases. Believing that securing and privatizing databases are two equilateral problems, in this paper, we propose a hybrid approach by combining Functional Encryption with the principles of Differential Privacy. Our main goal is not only to design a scheme for processing statistical data and releasing statistics in a privacy-preserving way but also to provide a richer, more balanced, and comprehensive approach in which data analytics and cryptography go hand in hand with a shift towards increased privacy.

In this work we develop a privacy-preserving reputation scheme for collaborative systems such as P2P networks in which peers can represent themselves with different pseudonyms when interacting with others. All these pseudonyms, however, are bound to the same reputation token, allowing honest peers to maintain their good record even when switching to a new pseudonym while preventing malicious ones from making a fresh start.

In this article, we present DPTS, a data payment and transfer the scheme that uses bitcoin payments to reward users for detailed electricity measurements they submit to a utility provider (UP). DPTS emphasizes both privacy and fairness of transactions; not only it allows participants to earn bitcoins in a way that cannot be linked to their actions or identities but also ensures that data are delivered if and only if an appropriate payment is received. While DPTS is described in the smart grid setting, the protocol can also be applied in other areas where incentives are used to increase user participation. One such important area is participatory or crowdsensing, where individuals use their smartphones to report sensed data back to a campaign administrator and obtain a reward for it. DPTS allows users to enjoy the benefits of participation without compromising anonymity. The proposal is coupled with a security analysis showing the privacy-preserving character of the system along with an efficiency analysis demonstrating the feasibility of our approach.

A useful functionality to motivate user engagement in participatory sensing applications is to provide for a privacy-preserving mechanism to reward users for the sensed data they submit to a utility provider. In this work we develop a lightweight token mechanism that allows participants to earn and redeem incentives in a way that cannot be linked to their actions or identities. Users do not have to maintain a collection of anonymous tokens but only a single one, which can be used to aggregate multiple rewards collected by them. This increases the privacy offered by the system. We have analyzed the security properties of our scheme and showed that our reward mechanism is privacy-respecting. Additionally, we have showed that it is highly efficient, outperforming previous solutions and guaranteeing good performance in practice.

The smart grid leverages infrastructural support to achieve fine-grained power consumption monitoring in an attempt to offer higher efficiency, reliability, and security. Such functionality, however, requires the collection of fine-grained usage data which may raise serious concerns with respect to consumer privacy. Thus far, existing work has solely focused on the problem of privately aggregating energy measurements. However, these solutions do not allow the provider to acquire detailed energy measurements which are essential for maintaining the network, debugging configuration problems, etc. In this work, we address this problem and we propose an authentication scheme that allows a smart meter to anonymously interact with the utility provider when submitting detailed consumption data. We then move one step further, enabling the incorporation of anonymous rewarding mechanisms in the smart grid in exchange for detailed measurements that users report. We argue that such rewarding mechanisms provide solid incentives for users to accept the release of their detailed energy consumption; we show that our proposal does notleak any information about the identity of users-even when redeeming the rewards. Finally, we implement a prototype based on our proposal and we evaluate its performance in realistic deployment settings.

Crowd-sensing applications are based on the contribution of user-related context information and as such, they are particularly vulnerable to privacy-compromising attacks. In this paper we focus on the problem of information discovery by data consumers who can pose queries to mobile users providing sensed data. The way to protect the privacy of these mobile users is through the use of cloud-based agents, which obfuscate user location and enforce the sharing practices of their owners. The cloud agents organise themselves in a structure, namely a quadtree, that enables queriers to contact directly the mobile users in the area of interest and, based on their own criteria, select the ones to get sensing data from. The tree is kept in a decentralized manner, stored and maintained by the mobile agents themselves, thus avoiding the privacy implications of previous, centralized techniques. Our proposed solution complements and expands upon prior work in the area while it is shown experimentally to be both scalable, efficient and easy to maintain.

An Unattended Wireless Sensor Network (UWSN) is a recently introduced type of sensor network in which real- time data delivery of information is replaced by periodic, offline collection of the sensed data. In this work we focus on UWSNs operating in a hostile environment where the goal of an attacker is to prevent targeted data from ever reaching the sink. More precisely, we study the Data Authentication problem in the presence of a Mobile Adversary who is aiming to target a sensor's data and modify it without being detected. Inspired by the pollination process in nature, we propose two schemes that diffuse data footprints in the network using message carrying "butterflies". These schemes greatly increase the robustness of the network against data modification at- tempts by the mobile adversary. Extensive analytical and experimental results confirm the superiority of both Pollination and Pollination Light over previous protocols in terms of both security and communication overhead.

ABSTRACT As the domains of pervasive computing and sensor networking are expanding, there is an ongoing trend towards assistive living and healthcare support environments that can effectively assimilate these technologies according to human needs. Most of the existing research in assistive healthcare follows a more passive approach and has focused on collecting and processing data using a static‐topology and an application‐aware infrastructure. However, with the technological advances in sensing, computation, storage, and communications, a new era is about to emerge changing the traditional view of sensor‐based assistive environments where people are passive data consumers, with one where people carry mobile sensing elements involving large volumes of data related to everyday human activities. This evolution will be driven by people‐centric sensing and will turn mobile phones into global mobile sensing devices enabling thousands new personal, social, and public sensing applications. In this paper, we discuss our vision for people‐centric sensing in assistive healthcare environments and study the security challenges it brings. This highly dynamic and mobile setting presents new challenges for information security, data privacy and ethics, caused by the ubiquitous nature of data traces originating from sensors carried by people. We aim to instigate discussion on these critical issues because people‐centric sensing will never succeed without adequate provisions on security and privacy. To that end, we discuss the latest advances in security and privacy protection strategies that hold promise in this new exciting paradigm. We hope this work will better highlight the need for privacy in people‐centric sensing applications and spawn further research in this area. Copyright © 2011 John Wiley & Sons, Ltd.

Malicious code (or malware) is defined as a software designed to execute attacks on software systems and fulfill the harmful intents of an attacker. As lightweight embedded devices become more ubiquitous and increasingly networked, they present a new and very disturbing target for malware developers. In this paper, we demonstrate how to execute malware on wireless sensor nodes that are based on the Von Neumann architecture. We achieve this by exploiting a buffer overflow vulnerability to smash the call stack and intrude a remote node over the radio channel. By breaking the malware into multiple packets, the attacker can inject arbitrarily long malicious code to the node and completely take control of it. Then we proceed to show how the malware can be crafted to become a self-replicating worm that broadcasts itself and infects the network in a hop-by-hop manner. To our knowledge, this is the first instance of a self-propagating worm that provides a detailed analysis along with instructions in order to execute arbitrary malicious code. We also provide a complete implementation of our attack, measure its effectiveness in terms of time taken for the worm to propagate to the entire sensor network and, finally, suggest possible countermeasures.

Radio frequency IDentification (RFID) is a method of remotely storing and retrieving data using small and inexpensive devices called RFID tags. In this work we propose a proxy agent framework that uses a personal device for privacy enforcement and increased protection against eavesdropping, impersonation and cloning attacks. Using the proxy a user decides when and where information carried in a tag will be released. In particular, the user can put tags under her control, authenticate requests, release tags, transfer them to new owners, and so on. This is the first framework that unifies previous attempts and presents detailed protocols for all the operations required in such a proxy environment.

In this paper, we propose a novel hybrid system for handling reputation in super-peer-based networks by combining the personal history of each user's interactions with other users, the opinions of peer-friends together with global ratings of peers as they emerge from all of their interactions with other users of the network. We introduce the notion of peer friends in a P2P network and use it to prevent malicious collectives from reducing the reputation of a peer in the network. We also present a secure distributed framework that ensures that trust reports remain encrypted and are never opened during the submission or aggregation process. Computational results from our distributed prototype simulation show that our solution compares favorably with all other proposed methods for handling reputation when subject to various malicious strategies.

In this work we introduce rfidDOT, a protocol for secure access, delegation and ownership transfer of tags along with a model for formally defining privacy in such an environment. As current RFID tags emit constant identifiers that may help in identifying user habits and tracking of people, rfidDOT allows a user to securely own tagged products. Once a person becomes the owner of such an item, no one can have access to the tag nor find any information about it. Thus user privacy is guaranteed. Additionally, the protocol is secure against such attacks as tag cloning, tag/reader spoofing, eavesdropping, desynchronization and so on. Furthermore, since we don't expect a tagged item to stay with same owner forever, we provide the means to achieve ownership transfer and release without compromising the privacy of future or past owners. And in the unlikely case where user privacy is compromised, it can be restored in a simple and intuitive manner. Thus rfidDOT achieves a very strong notion of security that is necessary in RFID ownership transfer: forward and backward privacy.

Wireless sensor networks are vulnerable to adversaries as they are frequently deployed in open and unattended environments. Preventive mechanisms can be applied to protect them from an assortment of attacks. However, more sophisticated methods, like intrusion detection systems, are needed to achieve a more autonomic and complete defense mechanism, even against attacks that have not been anticipated in advance. In this paper, we present a lightweight intrusion detection system, called LIDeA, designed for wireless sensor networks. LIDeA is based on a distributed architecture, in which nodes overhear their neighboring nodes and collaborate with each other in order to successfully detect an intrusion. We show how such a system can be implemented in TinyOS, which components and interfaces are needed, and what is the resulting overhead imposed.

One of the reasons that the research of intrusion detection in wireless sensor networks has not advanced significantly is that the concept of "intrusion" is not clear in these networks. In this paper we investigate in depth one of the most severe attacks against sensor networks, namely the sinkhole attack, and we emphasize on strategies that an attacker can follow to successfully launch such an attack. Then we propose specific detection rules that can make legitimate nodes become aware of the threat, while the attack is still taking place. Finally, we demonstrate the attack and present some implementation details that emphasize the little effort that an attacker would need to put in order to break into a realistic sensor network.

In this paper we introduce a link layer security platform for wireless sensor networks. At the heart of this platform, lies our key management module facilitating an efficient scalable post-distribution key establishment that allows the platform to provide different security services. We have developed this framework under TinyOs and have tested it with MICA2 motes. To the best of our knowledge this is the first implemented security platform for sensor networks that provides acceptable resistance against node capture attacks and replay attacks. The provision of security services is completely transparent to the user of the framework. Furthermore, being highly scalable and lightweight, this platform is appropriate to be used in a wireless sensor network of hundreds of nodes.

Wireless sensor networks have emerged as an exciting new area of research in computer science. Continuously shrinking battery powered nodes are equipped with processing, sensing and RF capabilities. However, deploying a network into a realistic environment requires iteratively reprogramming dozens of nodes, locating them throughout an area large enough to produce an interesting radio topology, and instrumenting them to extract debugging and performance data.

In this paper, we describe SuperTrust, a novel and efficient framework designed to handle trust relationships in Super-peer networks. What distinguishes SuperTrust from other works is that trust reports remain encrypted and are never opened during the submission or aggregation processes, thus guaranteeing privacy, anonymity, fairness, persistence and eligibility of transactions.

In this work we propose a solution to the RFID privacy problem that has the potential to guarantee user privacy without requiring changes to existing infrastructure or reducing business value from the use of RFID technology. We give emphasis to the development of a lightweight protocol that does not incur costly overheads with respect to computation, storage as well as time and effort needed for deployment configuration. For RFID technology to be widely used, security should ship as a "default" and require no significant effort to configure. We demonstrate the security and efficiency properties of our protocol and we offer some interesting time/space tradeoffs that may lead to further improvements

RFID identification is a new technology that will become ubiquitous as RFID tags will be applied to every-day items in order to yield great productivity gains or “smart” applications for users. However, this pervasive use of RFID tags opens up the possibility for various attacks violating user privacy. In this work we present an RFID authentication protocol that enforces user privacy and protects against tag cloning. We designed our protocol with both tag-to-reader and reader-to-tag authentication in mind; unless both types of authentication are applied, any protocol can be shown to be prone to either cloning or privacy attacks. Our scheme is based on the use of a secret shared between tag and database that is refreshed to avoid tag tracing. However, this is done in such a way so that efficiency of identification is not sacrificed. Additionally, our protocol is very simple and it can be implemented easily with the use of standard cryptographic hash functions. In analyzing our protocol, we identify several attacks that can be applied to RFID protocols and we demonstrate the security of our scheme. Furthermore, we show how forward privacy is guaranteed; messages seen today will still be valid in the future, even after the tag has been compromised.

We introduce a variant of Aldous and Vazirani's "Go with the winners" algorithm that can be used for search graphs that are not trees.We analyze the algorithm in terms of the properties of a tree-decomposition of the search graph.We show a large clazs of distributions for search graphs so that "Go with the winners" works well with high probability y for almost all graphs from the distribution.We also give a sufficient combinatorial property that ensures good performance.