Biblio

Polynomial masking is a glitch-resistant and higher-order masking scheme based upon Shamir's secret sharing scheme and multi-party computation protocols. Polynomial masking was first introduced at CHES 2011, while a 1st-order implementation of the AES S-box on FPGA was presented at CHES 2013. In this latter work, the authors showed a 2nd-order univariate leakage by side-channel collision analysis on a tuned measurement setup. This negative result motivates the need to evaluate the performance, area-costs, and security margins of combined \shuffled\ and higher-order polynomially masking schemes to counteract trivial univariate leakages. In this work, we provide the following contributions: first, we introduce additional principles for the selection of efficient addition chains, which allow for more compact and faster implementations of cryptographic S-boxes. Our 1st-order AES S-box implementation requires approximately 27% less registers, 20% less clock cycles, and 5% less random bits than the CHES 2013 implementation. Then, we propose a lightweight shuffling countermeasure, which inherently applies to polynomial masking schemes and effectively enhances their univariate security at negligible area expenses. Finally, we present the design of a \combined\ \shuffled\ \and\ higher-order polynomially masked AES S-box in hardware, while providing ASIC synthesis and side-channel analysis results in the Electro-Magnetic (EM) domain.

Eddy is a privacy requirements specification language that privacy analysts can use to express requirements over data practices; to collect, use, transfer and retain personal and technical information. The language uses a simple SQL-like syntax to express whether an action is permitted or prohibited, and to restrict those statements to particular data subjects and purposes. Eddy also supports the ability to express modifications on data, including perturbation, data append, and redaction. The Eddy specifications are compiled into Description Logic to automatically detect conflicting requirements and to trace data flows within and across specifications. Conflicts are highlighted, showing which rules are in conflict (expressing prohibitions and rights to perform the same action on equivalent interpretations of the same data, data subjects, or purposes), and what definitions caused the rules to conflict. Each specification can describe an organization's data practices, or the data practices of specific components in a software architecture.

Masking requires splitting sensitive variables into at least d+1 shares to provide security against DPA attacks at order d. To this date, this minimal number has only been deployed in software implementations of cryptographic algorithms and in the linear parts of their hardware counterparts. So far there is no hardware construction that achieves this lower bound if the function is nonlinear and the underlying logic gates can glitch. In this paper, we give practical implementations of the AES using d+1 shares aiming at first- and second-order security even in the presence of glitches. To achieve this, we follow the conditions presented by Reparaz et al. at CRYPTO 2015 to allow hardware masking schemes, like Threshold Implementations, to provide theoretical higher-order security with d+1 shares. The decrease in number of shares has a direct impact in the area requirements: our second-order DPA resistant core is the smallest in area so far, and its S-box is 50% smaller than the current smallest Threshold Implementation of the AES S-box with similar security and attacker model. We assess the security of our masked cores by practical side-channel evaluations. The security guarantees are met with 100 million traces.

There are two broad approaches for differentially private data analysis. The interactive approach aims at developing customized differentially private algorithms for various data mining tasks. The non-interactive approach aims at developing differentially private algorithms that can output a synopsis of the input dataset, which can then be used to support various data mining tasks. In this paper we study the effectiveness of the two approaches on differentially private k-means clustering. We develop techniques to analyze the empirical error behaviors of the existing interactive and non-interactive approaches. Based on the analysis, we propose an improvement of DPLloyd which is a differentially private version of the Lloyd algorithm. We also propose a non-interactive approach EUGkM which publishes a differentially private synopsis for k-means clustering. Results from extensive and systematic experiments support our analysis and demonstrate the effectiveness of our improvement on DPLloyd and the proposed EUGkM algorithm.

I will explain the linkage between threshold implementation masking schemes and multi-party computation. The basic principles that need to be taken from multi-party computation will be presented, as well as some basic protocols. The different natures of the resources and threat models between the two different applications of secret sharing will also be covered.

In settings where data instances are generated sequentially or in streaming fashion, online learning algorithms can learn predictors using incremental training algorithms such as stochastic gradient descent. In some security applications such as training anomaly detectors, the data streams may consist of private information or transactions and the output of the learning algorithms may reveal information about the training data. Differential privacy is a framework for quantifying the privacy risk in such settings. This paper proposes two differentially private strategies to mitigate privacy risk when training a classifier for anomaly detection in an online setting. The first is to use a randomized active learning heuristic to screen out uninformative data points in the stream. The second is to use mini-batching to improve classifier performance. Experimental results show how these two strategies can trade off privacy, label complexity, and generalization performance.

Outsourcing a huge amount of local data to remote cloud servers that has been become a significant trend for industries. Leveraging the considerable cloud storage space, industries can also put forward the outsourced data to cloud computing. How to collect the data for computing without loss of privacy and confidentiality is one of the crucial security problems. Searchable encryption technique has been proposed to protect the confidentiality of the outsourced data and the privacy of the corresponding data query. This technique, however, only supporting search functionality, may not be fully applicable to real-world cloud computing scenario whereby secure data search, share as well as computation are needed. This work presents a novel encrypted cloud-based data share and search system without loss of user privacy and data confidentiality. The new system enables users to make conjunctive keyword query over encrypted data, but also allows encrypted data to be efficiently and multiply shared among different users without the need of the "download-decrypt-then-encrypt" mode. As of independent interest, our system provides secure keyword update, so that users can freely and securely update data's keyword field. It is worth mentioning that all the above functionalities do not incur any expansion of ciphertext size, namely, the size of ciphertext remains constant during being searched, shared and keyword-updated. The system is proven secure and meanwhile, the efficiency analysis shows its great potential in being used in large-scale database.

In the area of the Internet of Things, cloud-based camera surveillance systems are ubiquitously available for industrial and private environments. However, the sensitive nature of the surveillance use case imposes high requirements on privacy/confidentiality, authenticity, and availability of such systems. In this work, we investigate how currently available mass-market camera systems comply with these requirements. Considering two attacker models, we test the cameras for weaknesses and analyze for their implications. We reverse-engineered the security implementation and discovered several vulnerabilities in every tested system. These weaknesses impair the users' privacy and, as a consequence, may also damage the camera system manufacturer's reputation. We demonstrate how an attacker can exploit these vulnerabilities to blackmail users and companies by denial-of-service attacks, injecting forged video streams, and by eavesdropping private video data - even without physical access to the device. Our analysis shows that current systems lack in practice the necessary care when implementing security for IoT devices.

Unease over data privacy will retard consumer acceptance of IoT deployments. The primary source of discomfort is a lack of user control over raw data that is streamed directly from sensors to the cloud. This is a direct consequence of the over-centralization of today's cloud-based IoT hub designs. We propose a solution that interposes a locally-controlled software component called a privacy mediator on every raw sensor stream. Each mediator is in the same administrative domain as the sensors whose data is being collected, and dynamically enforces the current privacy policies of the owners of the sensors or mobile users within the domain. This solution necessitates a logical point of presence for mediators within the administrative boundaries of each organization. Such points of presence are provided by cloudlets, which are small locally-administered data centers at the edge of the Internet that can support code mobility. The use of cloudlet-based mediators aligns well with natural personal and organizational boundaries of trust and responsibility.

With the increasing popularity of cloud storage services, many individuals and enterprises start to move their local data to the clouds. To ensure their privacy and data security, some cloud service users may want to encrypt their data before outsourcing them. However, this impedes efficient data utilities based on the plain text search. In this paper, we study how to construct a secure index that supports both efficient index updating and similarity search. Using the secure index, users are able to efficiently perform similarity searches tolerating input mistakes and update the index when new data are available. We formally prove the security of our proposal and also perform experiments on real world data to show its efficiency.

The wide presence of large graph data and the increasing popularity of storing data in the cloud drive the needs for graph query processing on a remote cloud. But a fundamental challenge is to process user queries without compromising sensitive information. This work focuses on privacy preserving subgraph matching in a cloud server. The goal is to minimize the overhead on both cloud and client sides for subgraph matching, without compromising users' sensitive information. To that end, we transform an original graph \$G\$ into a privacy preserving graph Gk, which meets the requirement of an existing privacy model known as k-automorphism. By making use of the symmetry in a k-automorphic graph, a subgraph matching query can be efficiently answered using a graph Go, a small subset of Gk. This approach saves both space and query cost in the cloud server. We also anonymize the query graphs to protect their label information using label generalization technique. To reduce the search space for a subgraph matching query, we propose a cost model to select the more effective label combinations. The effectiveness and efficiency of our method are demonstrated through extensive experimental results on real datasets.

The average computer user is no longer restricted to one device. They may have several devices and expect their applications to work on all of them. A challenge arises when these applications need the cryptographic private key of the devices' owner. Here the device owner typically has to manage keys manually with a "keychain" app, which leads to private keys being transferred insecurely between devices – or even to other people. Even with intuitive synchronization mechanisms, theft and malware still pose a major risk to keys. Phones and watches are frequently removed or set down, and a single compromised device leads to the loss of the owner's private key, a catastrophic failure that can be quite difficult to recover from. We introduce Shatter, an open-source framework that runs on desktops, Android, and Android Wear, and performs key distribution on a user's behalf. Shatter uses threshold cryptography to turn the security weakness of having multiple devices into a strength. Apps that delegate cryptographic operations to Shatter have their keys compromised only when a threshold number of devices are compromised by the same attacker. We demonstrate how our framework operates with two popular Android apps (protecting identity keys for a messaging app, and encryption keys for a note-taking app) in a backwards-compatible manner: only Shatter users need to move to a Shatter-aware version of the app. Shatter has minimal impact on app performance, with signatures and decryption being calculated in 0.5s and security proofs in 14s.

In this paper we describe a privacy-preserving method for commissioning an IoT device into a cloud ecosystem. The commissioning consists of the device proving its manufacturing provenance in an anonymous fashion without reliance on a trusted third party, and for the device to be anonymously registered through the use of a blockchain system. We introduce the ChainAnchor architecture that provides device commissioning in a privacy-preserving fashion. The goal of ChainAnchor is (i) to support anonymous device commissioning, (ii) to support device-owners being remunerated for selling their device sensor-data to service providers, and (iii) to incentivize device-owners and service providers to share sensor-data in a privacy-preserving manner.

UnlimitID is a method for enhancing the privacy of commodity OAuth and applications such as OpenID Connect, using anonymous attribute-based credentials based on algebraic Message Authentication Codes (aMACs). OAuth is one of the most widely used protocols on the Web, but it exposes each of the requests of a user for data by each relying party (RP) to the identity provider (IdP). Our approach allows for the creation of multiple persistent and unlinkable pseudo-identities and requires no change in the deployed code of relying parties, only in identity providers and the client.

The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in "Big Data" is apparent from their sharply divergent nature at an elementary level-in computer science, the growth of the number of data points is a source of "complexity" that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of "simplicity" in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as "runtime" in core statistical theory and the lack of a role for statistical concepts such as "risk" in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and ways to exploit parallelism so as to trade off the speed and accuracy of inference.

Standardization and harmonization efforts have reached a consensus towards using a special-purpose Vehicular Public-Key Infrastructure (VPKI) in upcoming Vehicular Communication (VC) systems. However, there are still several technical challenges with no conclusive answers; one such an important yet open challenge is the acquisition of short-term credentials, pseudonym: how should each vehicle interact with the VPKI, e.g., how frequently and for how long? Should each vehicle itself determine the pseudonym lifetime? Answering these questions is far from trivial. Each choice can affect both the user privacy and the system performance and possibly, as a result, its security. In this paper, we make a novel systematic effort to address this multifaceted question. We craft three generally applicable policies and experimentally evaluate the VPKI system performance, leveraging two large-scale mobility datasets. We consider the most promising, in terms of efficiency, pseudonym acquisition policies; we find that within this class of policies, the most promising policy in terms of privacy protection can be supported with moderate overhead. Moreover, in all cases, this work is the first to provide tangible evidence that the state-of-the-art VPKI can serve sizable areas or domain with modest computing resources.

Emergency evacuations during disasters minimize loss of lives and injuries. It is not surprising that emergency evacuation preparedness is mandatory for organizations in many jurisdictions. In the case of corporations, this requirement translates to considerable expenses, consisting of construction costs, equipment, recruitment, retention and training. In addition, required regular evacuation drills cause recurring expenses and loss of productivity. Any automation to assist in these drills and in actual evacuations can mean savings of costs, time and lives. Evacuation assistance systems rely on attendance systems that often fall short in accuracy, particularly in environments with lot of "non-swipers" (customers, visitors, etc.,). A critical question to answer in the case of an emergency is "How many people are still in the building?". This number is calculated by comparing the number of people gathered at assembly point to the last known number of people inside the building. An IoT based system can enhance the answer to that question by providing the number of people in the building, provide their last known locations in an automated fashion and even automate the reconciliation process. Our proposed system detects the people in the building automatically using multiple channels such as WiFi and motion detection. Such a system needs the ability to link specific identifiers to persons reliably. In this paper we present our statistics and heuristics based solutions for linking detected identifiers as belonging to an actual persons in a privacy preserving manner using IoT technologies.

Behavior-based tracking is an unobtrusive technique that allows observers to monitor user activities on the Internet over long periods of time – in spite of changing IP addresses. Previous work has employed supervised classifiers in order to link the sessions of individual users. However, classifiers need labeled training sessions, which are difficult to obtain for observers. In this paper we show how this limitation can be overcome with an unsupervised learning technique. We present a modified k-means algorithm and evaluate it on a realistic dataset that contains the Domain Name System (DNS) queries of 3,862 users. For this purpose, we simulate an observer that tries to track all users, and an Internet Service Provider that assigns a different IP address to every user on every day. The highest tracking accuracy is achieved within the subgroup of highly active users. Almost all sessions of 73% of the users in this subgroup can be linked over a period of 56 days. 19% of the highly active users can be traced completely, i.e., all their sessions are assigned to a single cluster. This fraction increases to 40% for shorter periods of seven days. As service providers may engage in behavior-based tracking to complement their existing profiling efforts, it constitutes a severe privacy threat for users of online services. Users can defend against behavior-based tracking by changing their IP address frequently, but this is cumbersome at the moment.

Unlike most social media, where automatic archiving of data is the default, Snapchat defaults to ephemerality: deleting content shortly after it is viewed by a receiver. Interviews with 25 Snapchat users show that ephemerality plays a key role in shaping their practices. Along with friend-adding features that facilitate a network of mostly close relations, default deletion affords everyday, mundane talk and reduces self-consciousness while encouraging playful interaction. Further, although receivers can save content through screenshots, senders are notified; this selective saving with notification supports complex information norms that preserve the feel of ephemeral communication while supporting the capture of meaningful content. This dance of giving and taking, sharing and showing, and agency for both senders and receivers provides the basis for a rich design space of mechanisms, levels, and domains for ephemerality.

Resource discovery in unstructured peer-to-peer networks causes a search query to be flooded throughout the network via random nodes, leading to security and privacy issues. The owner of the search query does not have control over the transmission of its query through the network. Although algorithms have been proposed for policy-compliant query or data routing in a network, these algorithms mainly deal with authentic route computation and do not provide mechanisms to actually verify the network paths taken by the query. In this work, we propose an approach to deal with the problem of verifying network paths taken by a search query during resource discovery, and detection of malicious forwarding of search query. Our approach aims at being secure and yet very scalable, even in the presence of huge number of nodes in the network.

Mobile devices, such as smarthphones, became a common tool in our daily routine. Mobile Applications (a.k.a. apps) are demanding access to contextual information increasingly. For instance, apps require user's environment data as well as their profiles in order to adapt themselves (interfaces, services, content) according to this context data. Mobile apps with this behavior are known as context-aware applications (CAS). Several software infrastructures have been created to help the development of CAS. However, most of them do not store the contextual data, once mobile devices are resource constrained. They are not built taking into account the privacy of contextual data either, due the fact that apps may expose contextual data, without user consent. This paper addresses these topics by extending an existing middleware platform that help the development of mobile context-aware applications. Our extension aims at store and process the contextual data generated from several mobile devices, using the computational power of the cloud, and the definition of privacy policies, which avoid dissemination of unauthorized contextual data.

A major component of modern vehicles is the infotainment system, which interfaces with its drivers and passengers. Other mobile devices, such as handheld phones and laptops, can relay information to the embedded infotainment system through Bluetooth and vehicle WiFi. The ability to extract information from these systems would help forensic analysts determine the general contents that is stored in an infotainment system. Based off the data that is extracted, this would help determine what stored information is relevant to law enforcement agencies and what information is non-essential when it comes to solving criminal activities relating to the vehicle itself. This would overall solidify the Intelligent Transport System and Vehicular Ad Hoc Network infrastructure in combating crime through the use of vehicle forensics. Additionally, determining the content of these systems will allow forensic analysts to know if they can determine anything about the end-user directly and/or indirectly.

We present D-ForenRIA, a distributed forensic tool to automatically reconstruct user-sessions in Rich Internet Applications (RIAs), using solely the full HTTP traces of the sessions as input. D-ForenRIA recovers automatically each browser state, reconstructs the DOMs and re-creates screenshots of what was displayed to the user. The tool also recovers every action taken by the user on each state, including the user-input data. Our application domain is security forensics, where sometimes months-old sessions must be quickly reconstructed for immediate inspection. We will demonstrate our tool on a series of RIAs, including a vulnerable banking application created by IBM Security for testing purposes. In that case study, the attacker visits the vulnerable web site, and exploits several vulnerabilities (SQL-injections, XSS...) to gain access to private information and to perform unauthorized transactions. D-ForenRIA can reconstruct the session, including screenshots of all pages seen by the hacker, DOM of each page and the steps taken for unauthorized login and the inputs hacker exploited for the SQL-injection attack. D-ForenRIA is made efficient by applying advanced reconstruction techniques and by using several browsers concurrently to speed up the reconstruction process. Although we developed D-ForenRIA in the context of security forensics, the tool can also be useful in other contexts such as aided RIAs debugging and automated RIAs scanning.

After more than a decade of research, web application security continues to be a challenge and the backend database the most appetizing target. The paper proposes preventing injection attacks against the database management system (DBMS) behind web applications by embedding protections in the DBMS itself. The motivation is twofold. First, the approach of embedding protections in operating systems and applications running on top of them has been effective to protect this software. Second, there is a semantic mismatch between how SQL queries are believed to be executed by the DBMS and how they are actually executed, leading to subtle vulnerabilities in prevention mechanisms. The approach – SEPTIC – was implemented in MySQL and evaluated experimentally with web applications written in PHP and Java/Spring. In the evaluation SEPTIC has shown neither false negatives nor false positives, on the contrary of alternative approaches, causing also a low performance overhead in the order of 2.2%.

In this paper, we have mentioned a method to find the performance of projectwhich detects various web - attacks. The project is capable to identifying and preventing attacks like SQL Injection, Cross – Site Scripting, URL rewriting, Web server 400 error code etc. The performance of system is detected using the system attributes that are mentioned in this paper. This is also used to determine efficiency of the system.