Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

/pdf/a-survey-of-mobile-phone-sensing-4to5zwn4tr.pdf

A survey of mobile phone sensing

Usually, a proof of a theorem contains more knowledge than the mere fact that the theorem is true. For instance, to prove that a graph is Hamiltonian it suffices to exhibit a Hamiltonian tour in it; however, this seems to contain more knowledge than the single bit Hamiltonian/non-Hamiltonian.In this paper a computational complexity theory of the “knowledge” contained in a proof is developed. Zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question. Examples of zero-knowledge proof systems are given for the languages of quadratic residuosity and 'quadratic nonresiduosity. These are the first examples of zero-knowledge proofs for languages not known to be efficiently recognizable.

/pdf/the-knowledge-complexity-of-interactive-proof-systems-31apre0ecf.pdf

The knowledge complexity of interactive proof-systems

An emerging category of devices at the edge of the Internet are consumer-centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensors. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this article, we examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. We present a brief overview of existing mobile crowdsensing applications, explain their unique characteristics, illustrate various research challenges, and discuss possible solutions. Finally, we argue the need for a unified architecture and envision the requirements it must satisfy.

Mobile crowdsensing: current state and future challenges

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

We consider the problem of efficiently proving the integrity of data stored at untrusted servers. In the provable data possession (PDP) model, the client preprocesses the data and then sends it to an untrusted server for storage, while keeping a small amount of meta-data. The client later asks the server to prove that the stored data has not been tampered with or deleted (without downloading the actual data). However, the original PDP scheme applies only to static (or append-only) files.We present a definitional framework and efficient constructions for dynamic provable data possession (DPDP), which extends the PDP model to support provable updates to stored data. We use a new version of authenticated dictionaries based on rank information. The price of dynamic updates is a performance change from O(1) to O(logn) (or O(nelog n), for a file consisting of n blocks, while maintaining the same (or better, respectively) probability of misbehavior detection. Our experiments show that this slowdown is very low in practice (e.g. 415KB proof size and 30ms computational overhead for a 1GB file). We also show how to apply our DPDP scheme to outsourced file systems and version control systems (e.g. CVS).

/pdf/dynamic-provable-data-possession-4zeysrt90l.pdf

Dynamic provable data possession

Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications.We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder.

/pdf/anonysense-privacy-aware-people-centric-sensing-379rjlrc9b.pdf

Anonysense: privacy-aware people-centric sensing

We put forth the problem of delegating the evaluation of a pseudorandom function (PRF) to an untrusted proxy and introduce a novel cryptographic primitive called delegatable pseudorandom functions, or DPRFs for short: A DPRF enables a proxy to evaluate a pseudorandom function on a strict subset of its domain using a trapdoor derived from the DPRF secret key. The trapdoor is constructed with respect to a certain policy predicate that determines the subset of input values which the proxy is allowed to compute. The main challenge in constructing DPRFs is to achieve bandwidth efficiency (which mandates that the trapdoor is smaller than the precomputed sequence of the PRF values conforming to the predicate), while maintaining the pseudorandomness of unknown values against an attacker that adaptively controls the proxy. A DPRF may be optionally equipped with an additional property we call policy privacy, where any two delegation predicates remain indistinguishable in the view of a DPRF-querying proxy: achieving this raises new design challenges as policy privacy and bandwidth efficiency are seemingly conflicting goals.For the important class of policy predicates described as (1-dimensional) ranges, we devise two DPRF constructions and rigorously prove their security. Built upon the well-known tree-based GGM PRF family, our constructions are generic and feature only logarithmic delegation size in the number of values conforming to the policy predicate. At only a constant-factor efficiency reduction, we show that our second construction is also policy private. Finally, we describe that their new security and efficiency properties render our DPRF schemes particularly useful in numerous security applications, including RFID, symmetric searchable encryption, and broadcast encryption.

/pdf/delegatable-pseudorandom-functions-and-applications-3mnyamlcxv.pdf

Delegatable pseudorandom functions and applications

We put forth the problem of delegating the evaluation of a pseudorandom function (PRF) to an untrusted proxy and introduce a novel cryptographic primitive called delegatable pseudorandom functions, or DPRFs for short: A DPRF enables a proxy to evaluate a pseudorandom function (PRF) on a strict subset of its domain using a trapdoor derived from the DPRF secret key. The trapdoor is constructed with respect to a certain policy predicate that determines the subset of input values which the proxy is allowed to compute. The main challenge in constructing DPRFs is to achieve bandwidth efficiency (which mandates that the trapdoor is smaller than the precomputed sequence of the PRF values conforming to the predicate), while maintaining the pseudorandomness of unknown values against an attacker that adaptively controls the proxy. A DPRF may be optionally equipped with an additional property we call policy privacy, where any two delegation predicates remain indistinguishable in the view of a DPRFquerying proxy: achieving this raises new design challenges as policy privacy and bandwidth efficiency are seemingly conflicting goals. For the important class of policy predicates described as (1-dimensional) ranges, we devise two DPRF constructions and rigorously prove their security. Built upon the well-known tree-based GGM PRF family [17], our constructions are generic and feature only logarithmic delegation size in the number of values conforming to the policy predicate. At only a constant-factor efficiency reduction, we show that our second construction is also policy private. Finally, we describe that their new security and efficiency properties render our DPRF schemes particularly useful in numerous security applications, including RFID, symmetric searchable encryption, and broadcast encryption.

/pdf/delegatable-pseudorandom-functions-and-applications-4b5rmpg42a.pdf

Delegatable Pseudorandom Functions and Applications.

https://dxoig.mn/papers/pmc-anonysense.pdf

AnonySense: A system for anonymous opportunistic sensing

Hash tables are fundamental data structures that optimally answer membership queries. Suppose a client stores n elements in a hash table that is outsourced at a remote server so that the client can save space or achieve load balancing. Authenticating the hash table functionality, i.e., verifying the correctness of queries answered by the server and ensuring the integrity of the stored data, is crucial because the server, lying outside the administrative control of the client, can be malicious.We design efficient and secure protocols for optimally authenticating membership queries on hash tables: for any fixed constants 0 1/e, the server can provide a proof of integrity of the answer to a (non-)membership query in constant time, requiring O(ne/logκe--1n) time to treat updates, yet keeping the communication and verification costs constant. This is the first construction for authenticating a hash table with constant query cost and sublinear update cost. Our solution employs the RSA accumulator in a nested way over the stored data, strictly improving upon previous accumulator-based solutions. Our construction applies to two concrete data authentication models and lends itself to a scheme that achieves different trade-offs---namely, constant update time and O(ne/logκen) query time for fixed e > 0 and κ > 0. An experimental evaluation of our solution shows very good scalability.

/pdf/authenticated-hash-tables-ppipuep286.pdf

Nikos Triandopoulos

Papers

Anonysense: privacy-aware people-centric sensing

Delegatable pseudorandom functions and applications

Delegatable Pseudorandom Functions and Applications.

AnonySense: A system for anonymous opportunistic sensing

Authenticated hash tables